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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931-940 订阅

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A hybrid model for short-time wind speed prediction based on a blended method 7

A hybrid model for short-time wind speed prediction based on...

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

作者： Feng, Jiaying Zou, Menghan Zhang, Yunzhu Faculty of Computing Harbin Institute of Technology Harbin264209 China Tiangong University School of Mathematical Sciences Tianjin300387 China

ISBN: (纸本)9781665478571

Wind speed forecasting that is accurate and reliable is critical for maximizing the economic benefits of wind power generation systems. On the other hand, individual forecasting models are insufficient for the reliable assessment of varied wind speed time series. A new hybrid wind speed forecast model with three elements is proposed in this research. For starters, data noise is reduced using an approach that combines SSA and phase space reconstruction. Predictions are made using a system that combines four artificial intelligence algorithms, including SVR, BPNN, RNN, and TCN, as well as powerful optimization techniques. Finally, two experiments were carried out using actual data from a wind farm in the United States, and the findings suggest that the hybrid model is more effective than traditional models, making wind energy dispatch and management easier in practice. © 2022 IEEE.

关键词： Speed

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Optimizing Insole Design for Improved Plantar Stress Distribution through Finite Element Analysis 7

Optimizing Insole Design for Improved Plantar Stress Distrib...

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7th international conference on Biological Information and Biomedical engineering, BIBE 2024

作者： Liu, Anchen Yang, Guyeli Pang, Xin Zhu, Jun Ma, Xin Huang, Ran Academy for Engineering and Technology Yiwu Research Institute Fudan University Shanghai200433 China Shanghai Sixth People's Hospital Shanghai Jiao Tong University Shanghai200233 China Centre for Innovation and Entrepreneurship Taizhou Institute of Zhejiang University Zhejiang Taizhou318000 China

ISBN: (纸本)9783800762910

this study explores the interaction between personalized insoles and plantar soft tissue (PST) using Finite Element Analysis (FEA) to optimize insole design for enhanced comfort and performance. Poorly-fitting footwear can lead to significant foot health issues, especially for diabetic foot care, including deformities and ulcers. Personalized insoles are increasingly favoured, yet their design often relies on subjective measures rather than objective data. this research employs FEA to simulate stress and strain distributions between the foot and insole under various loading conditions, examining how different insole material properties, thicknesses, and PST characteristics influence these interactions. the findings reveal that the modulus of the PST plays a crucial role in stress distribution, suggesting that insoles should be tailored to individual PST conditions for optimal fit. Based on the exploration of this study, further investigation in reality is expected to establish a comprehensive material reference table to guide the selection of insole materials, aiming to improve therapeutic outcomes and overall foot health through scientifically informed insole design, particularly in terms of enhancing comfort, reducing injury risk across various populations, and preventing plantar-related conditions. © VDE VERLAG GMBH.

关键词： Stress concentration

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Research on an Adaptive RRT Path Planning Algorithm Based on Regional Sampling 7

Research on an Adaptive RRT Path Planning Algorithm Based on...

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

作者： Su, Yu Li, Hongbing Gong, Wenbin Chen, Honghao Jiang, Yutao Long, Boxuan Chongqing Three Gorges University College of Computer Science and Engineering Chongqing Wanzhou China Chongqing Three Gorges University College of Electronic and Information Engineering Chongqing Wanzhou China Changchun University of Science and Technology College of Computer Science and Technology Jilin Changchun China

ISBN: (纸本)9798350375107

An enhanced RRT path planning algorithm based on regional sampling is proposed with the goal of addressing the issues of inadequate guidance, discontinuous sampling, and unsatisfactory path in RRT algorithm path search in complicated situations. the improved algorithm first enhances the algorithm's guidance and avoids unnecessary sampling by using a target bias strategy and a regional sampling strategy. Secondly, an adaptive step size strategy based on obstacle factors is adopted to dynamically adjust the step size by detecting the complexity of the environment around the expansion direction. Finally, the produced path is trimmed and optimized. According to simulation results, the revised algorithm reduces running time, path length, and number of path nodes by 68.65%, 18.56%, and 85.45%, respectively, in comparison to the RRT algorithm. the enhanced approach reduces the running time, path length, and number of path nodes by 14.06%, 12.91%, and 82.97%, respectively, in comparison to the Bias RRT algorithm. © 2024 IEEE.

关键词： Signal sampling

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A Fine-grained volley gesture recognition method with Direction Independence 23

A Fine-grained volley gesture recognition method with Direct...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Hao, Zhanjun Liu, Gaoyuan Dang, Xiaochao Zhang, Daiyang Bai, Yanhong Xu, Hongwen Northwest Normal University School of Computer Science and Engineering LanZhou China

ISBN: (纸本)9781665494571

the development of wireless sensing technology makes it possible to control various IoT devices with gestures. In the current severe international epidemic situation, the use of non-contact gesture recognition methods can greatly reduce direct or indirect contact between people and effectively curb the spread of the virus. However, since the direction, position, and motion range of each person's gesture are different, it brings great challenges to the accurate recognition of gestures. To solve this problem, this paper proposes a direction-independent fine-grained volley gesture recognition method CSI-VGR based on Wi-Fi signal sensing technology. CSI-VGR first converts the CSI signal of a gesture into a Doppler shift. In particular, CSI-VGR uses a temporal convolutional network (TCN) to segment the gesture action and extract the features of each gesture. Finally, the Echo State Network (ESN) is introduced, and the gesture feature is input to obtain the classification result of the gesture. this paper verifies the performance of CSI-VGR in multiple dimensions in two real scenarios. Experimental results show that the gesture recognition accuracy of the CSI-VGR method can reach 96.3%, and it shows satisfactory robustness in both scenarios. © 2021 IEEE.

关键词： Wireless communication Wireless sensor networks Smoothing methods Convolution Gesture recognition Interference Feature extraction

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Cutting-Edge Communication: Integrated Satellite-Aerial 6G Networks for Point-to-Point Connectivity 7

Cutting-Edge Communication: Integrated Satellite-Aerial 6G N...

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

作者： Kumar, Keerthipati Vani, V Divya Raj, Vijilius Helena Dutt, Amit Kunekar, Pankaj Sahu, Dilip Narayan Sv College of Engineering Karkambadi Andhra Pradesh India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Lovely Professional University Punjab Phagwara India Vishwakarma Institute of Technology Maharashtra Pune India Gangadhar Meher University Department of Mca Odisha India

ISBN: (纸本)9798350350067

Upcoming mobile networking will require wireless applications with global coverage and better data rates. Current diverse cellular systems like wireless and satellite systems are not able to satisfy these demands on their own in particularly remote places with mountains and seas. this paper uses transdisciplinary expertise, including domain knowledge of 6 G satellite communication, advanced NLP technology, and patent retrieval and analytic approaches, to identify the potential technological landscape and trends. Depending on the abovementioned objectives, this study offered an exhaustive evaluation of ISAC innovation for 6G ISTNs in this study. Initially, this investigation provided an outline of the main technologies, assessment criteria, and past development history of the ISAC. After that, a thorough discussion of the state of ISTN development was held, emphasizing integrated network topologies. Finally, we outline the key technologies and scenarios that could be implemented in the next generation of satellite-terrestrial ISAC. © 2024 IEEE.

关键词： Satellite communication systems

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RSI-Mix: Data Augmentation Method for Remote Sensing Image Classification 7

RSI-Mix: Data Augmentation Method for Remote Sensing Image C...

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

作者： Lu, Xingshun Zhang, Chao Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

Data augmentation is a common strategy to improve the performance of computer vision tasks. Regrettably, current data augmentation methods are often designed for images in RGB format and few are studied for remote sensing images. In this paper, we find that the way in which remote sensing data are obtained provides a realistic possibility for cropping images to create new ones. Based on our analyses, we propose RSI-Mix for Sentinel-2 satellite image classification. RSI-Mix is designed to cut and paste two remote sensing images of the same category according to random masks. the key inspiration of RSI-Mix is that the classification of remote sensing images is not strictly based on image texture but based on band features. the information fusion of the same area from different sources is beneficial to make an area contain more band features. Experiments show that the model with RSI-Mix is more stable and has higher performance. © 2022 IEEE.

关键词： Image classification

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. the comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. the results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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Snow Leopard Optimization Algorithm-Based Energy Efficient Cluster Head Selection in Internet of things 7th

Snow Leopard Optimization Algorithm-Based Energy Efficient C...

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7th international conference on Advanced computing and Intelligent engineering, ICACIE 2022

作者： Arunachalam, N. Sengathir, Janakiraman Priya, M. Deva Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9789819950140

Energy stability and network lifetime are most challenging aspects in Internet of things (IoT) in todays’ smart world. Clustering is an effective option for sustaining energy stability and network lifetime in IoT as the process of grouping nodes into clusters with efficient cluster head (CH) selection increases the probability of dramatically reducing energy consumption. However, potential selection of CHs is necessary in IoT as it is widely adopted in crucial applications which include smart agriculture and forest monitoring. In this paper, Snow Leopard Optimization Algorithm-based CH Selection Strategy (SLOACHSS) is proposed for maximizing energy stability and network lifespan by achieving optimized data routing in IoT. this proposed SLOACHSS achieves the process of clustering using steps that includes selection of CHs using SLOA and cluster formation. the process of CHs selection is achieved through the application of two different fitness functions that include energy possessed by CHs, and factor of inter-cluster and intra-cluster distance. SLOACHSS is proposed with phases of traveling of routes, hunting, reproduction, and mortality for balancing the rate of exploration and exploitation during CH selection in IoT. the simulation experiments of the proposed SLOACHSS confirm 23.21% improved energy consumption and 21.98% improved throughput in contrast to standard schemes for varying number of sensor nodes in the network. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Physical Layer Secret Key Generation for Spatially Correlated Channels Based on Multi-Task Autoencoder 7

Physical Layer Secret Key Generation for Spatially Correlate...

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

作者： Zhou, Jingqi Zeng, Xin Tongji University Dept. of Automation Shanghai China Tongji University Dept. of Information and Communication Engineering Shanghai China

ISBN: (纸本)9781665478571

Physics layer key, as an emerging method to enhance information security, is generated based on the reciprocity and spatial decorrelation of wireless channels. However, due to the half-duplex mode of the communication systems, the channels detected by the legitimate sides are not completely reciprocal. Meanwhile, when the eavesdropper is extremely close to the legitimate side, the spatial correlation could be very strong and threaten the information security. this paper combines the autoencoder and the multi-task learning into a novel physical layer key generation scheme, i.e., Multi-Task Autoencoder (MTAE). MTAE extracts the reciprocal features from the weakly correlated channel estimates of the legitimate sides while reducing its correlation with eavesdropper. Simulation results show good randomness and consistency with the primary keys generated based on MTAE for the legitimate sides, while increasing the difference in the extracted features between eavesdropper and its adjacent node. the proposed method has great potential in scenarios requiring high security. © 2022 IEEE.

关键词： Learning systems

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the Role of Edge computing in Enhancing Cloud Performance and Reducing Latency 7

The Role of Edge Computing in Enhancing Cloud Performance an...

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

作者： Sharma, Puneet Shyamala Devi, J. Manikandan, K. Ani Minisha, R. Harini, R. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

the flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. this research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. the work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. the results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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