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检索条件"任意字段=International Conference on Computing and Convergence Technology"

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Nonmonotone Accelerated Manifold Proximal Gradient with Adaptive Stepsize Method for Sparse PCA with Application to Dimensionality Reduction

Nonmonotone Accelerated Manifold Proximal Gradient with Adap...

引用

2022 international conference on Image Processing and Media computing, ICIPMC 2022

作者： Zhang, Xinyu Yan, Tao Wang, Kai Nanjing University of Science and Technology School of Mathematics and Statistics Nanjing China

ISBN: (数字)9781665468725

ISBN: (纸本)9781665468725

Sparse principal component analysis (SPCA) has attracted attention in many areas such as signal/image processing, statistics, bioinformatics and machine learning. In this paper, we develop a new accelerated proximal gradient method on Stiefel manifold for SPCA. We apply the nonmonotone accelerate technique which can reduce the iteration and improve the convergence rate of algorithm. Numerical experiments show that the proposed method can get better results for some test data. © 2022 IEEE.

关键词： Numerical methods

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A Many-to-One Matching based Task Offloading (MATO) Scheme for Fog computing-enabled IoT Systems 15

A Many-to-One Matching based Task Offloading (MATO) Scheme f...

引用

15th international conference on Advanced Technologies for Communications, ATC 2022

作者： Tran-Dang, Hoa Kim, Dong-Seong Kumoh National Institute of Technology Dept. of It Convergence Engineering Gumi-si Korea Republic of

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

Fog computing networks have been widely integrated in IoT-based systems to improve the quality of services (QoS) such as low response service delay through efficient offloading algorithms. However, designing an efficient offloading solution is still facing many challenges including the complicated heterogeneity of fog computing devices and complex computation tasks. In addition, the need for a scalable and distributed algorithm with low computational complexity can be unachievable by global optimization approaches with centralized information management in the dense fog networks. In these regards, this paper proposes a distributed computation offloading framework (MATO) for offloading the splittable tasks using matching theory. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems compared to some related works. © 2022 IEEE.

关键词： Fog computing

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Smart Teacher - An AI-Based Self-Learning Platform for Grade 10 ICT Students 7

Smart Teacher - An AI-Based Self-Learning Platform for Grade...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Jathushan, V. Senthujan, B. Ajiharan, B. Kasthury, M. Gamage, Anjali Ganegoda, Devanshi Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665421683

E-learning is a method of learning that uses electronic resources but is based on institutionalized education. While education can take place in or outside of the classroom, E-learning relies heavily on computers and the Internet. The smart Teacher is a self-learning platform for ICT students that is powered by artificial intelligence. The study's purpose is to enhance teaching and learning functions by allowing for the creation and storage of learning materials, as well as their availability and accessibility. We concentrated our research on the Grade 10 ICT topic. We created a web application that Grade 10 students can use to self- We created a web application that Grade 10 students can use to self-teach and self-assess their ICT skills, and it will be free. It will result in a higher pass rate for fundamental ICT *** can ask questions on the main page (Image). We assigned ratings to the responses provided by different users and automatically ordered the responses based on the ratings. We'll offer real-time cautions about unrelated questions. Simultaneously, we added a chatbot that will provide related site recommendations as well as real-time website instructions. It will listen to the students' queries (voice or sound) and then propose relevant questions in text form that the students can utilize. © 2022 IEEE.

关键词： Students

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Feature extraction and application of signal weak shock based on particle swarm optimization neural network development and verification

Feature extraction and application of signal weak shock base...

引用

2022 international conference on Information System, computing and Educational technology, ICISCET 2022

作者： Xiang, Li Shandong Vocational and Technical University of International Studies Shandong Rizhao276826 China

ISBN: (数字)9781665460446

ISBN: (纸本)9781665460446

When a gear fails, it will produce regular shock vibration. By detecting whether the signal contains fault shock, bearing state identification and fault diagnosis can be realized. Aiming at the detection of weak impact signal of gear fault and determination of true characteristic frequency, a method of signal weak impact feature extraction based on PSONN (Particle Swarm Optimization Neural Network) is proposed. On the basis of studying PSONN, training BPNN can greatly improve the convergence speed of the network and avoid falling into the local optimal solution. Through the simulation study of weak shock feature extraction in strong background signal and noise environment, it is proved that this method has good shock feature extraction ability. The effectiveness of this method is proved by the analysis results of measured gear tooth breaking signals. © 2022 IEEE.

关键词： Feature extraction

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Multivariate Deep Learning Model For Workload Prediction In Cloud computing 12

Multivariate Deep Learning Model For Workload Prediction In ...

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12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Dang-Quang, Nhat-Minh Yoo, Myungsik Soongsil Univ Dept Informat Commun Convergence Technol Seoul South Korea Soongsil Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665423830

Many application developers are now choosing to install their Web applications on cloud data centers because of the attractiveness of cloud computing environment. Predicting future resource workload is critical since it allows cloud service providers to automatically modify resources online in order to meet service level agreements (SLA). This paper proposes a multivariate deep learning prediction model to predict future resource workload for cloud computing environment. The prediction model uses a special type of recurrent neural network (RNN) called Bidirectional long short-term memory (Bi-LSTM). This work also explains and shows the advantage by using multivariate data compared to univariate data in time series forecasting. The experiments, using real world workload dataset, show that the proposed multivariate Bi-LSTM model outperforms the univariate Bi-LSTM model in prediction accuracy.

关键词： Workload prediction time series forecasting cloud computing

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Modulation of Resistive Switching Behaviour of TaOx-based Memristor Through Device Engineering 7

Modulation of Resistive Switching Behaviour of TaOx-based Me...

引用

7th IEEE international conference for convergence in technology, I2CT 2022

作者： Poojith, Kummara Varshney, Kanupriya Yadav, Mani Shankar Das, Devarshi Rawat, Brajesh Indian Institute of Technology Department of Electrical Engineering Ropar140001 India

ISBN: (纸本)9781665421683

Tantalum oxide (TaOx)-based memristor has been emerged as a promising candidate for non-volatile memory and neuromorphic computing system as it offers low-power and high-speed operations with possibility of high-density integration in 3-D crossbar array. Although many studies have reported bipolar resistive switching behavior, a critical understanding of the factor affecting the switching behavior is still missing. Therefore, in this work, we examine the impact of device design parameters, such as activation energy of conduction, filament radius, and oxide thickness, on the resistive switching behavior and its implication on non-volatile crossbar memory. The device performance investigation is done using fully calibrated numerical simulation model, based on self-consistent solutions of current continuity, heat transport and continuity equations for accurately capturing field and temperature-assist migration of oxygen vacancy. The results shows that a lower values of filament radius and activation energy are right choices for achieving a lower sneak current and hence improve the readout margin for crossbar array;however, the optimum selection of filament radius is important as it reduces switching current and increases the read voltage. We also investigate the scaling behavior of the resistive layer thickness. © 2022 IEEE.

关键词： Activation energy

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Enhancing In-Vehicle Network Security Through Bitstream Feature Extraction-Based Intrusion Detection 2023

Enhancing In-Vehicle Network Security Through Bitstream Feat...

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15th international conference on Contemporary computing, IC3 2023

作者： Islam, Md Rezanur Ugli, Muminov Ibrokhim Botir Oh, Insu Yim, Kangbin Department of Software Convergence Soonchunhyang University Asan-si Chungnam Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan-si Chungnam Korea Republic of Department of Information Security Engineering Soonchunhyang University Asan-si Chungnam Korea Republic of

ISBN: (纸本)9798400700224

The in-vehicle network (IVN) is highly vulnerable due to its inherent structure, and the continuous introduction of new features in next-generation vehicles only exacerbates this issue. To address this problem, a proposed intrusion detection approach for in-vehicle networks is based on bitstream feature extraction and autocorrelation analysis. This approach aims to overcome the limitations associated with memory utilization and processing overhead vulnerability by comparing payload features using nibble. The Controller Area Network (CAN) data is highly susceptible, and organizations worldwide are working on developing Intrusion Detection Systems (IDSs) to protect vehicles. Nibbles are four-bit units of digital information, and autocorrelation measures the correlation of a variable with itself over time. In the proposed approach, the CAN payload is processed using nibble feature extraction and fed into a deep-learning CNN for attack classification. This approach presents a promising solution for enhancing the security of in-vehicle networks, by utilizing advanced machine-learning techniques to improve the security of vehicle systems. © 2023 ACM.

关键词： Feature extraction

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Optimization Study of BP Neural Network Based on Genetic Algorithm

Optimization Study of BP Neural Network Based on Genetic Alg...

引用

2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Cai, Yongheng Zhou, Zhuye Li, Zehui School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350309614

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R2), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios. © 2023 IEEE.

关键词： BP neural network genetic algorithm optimization algorithm

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FedRC: A Federated Learning-Based Roadside computing Paradigm Through the Facilitation of Internet of Drones 12

FedRC: A Federated Learning-Based Roadside Computing Paradig...

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12th international conference on ICT convergence (ICTC) - Beyond the Pandemic Era with ICT convergence Innovation

作者： Islam, Anik Shin, Soo Young Kumoh Natl Inst Technol KIT MERIT Med Elect Robot IT Convergence Innovat Talents Training Grp Smart Mf Gumi South Korea Kumoh Natl Inst Technol KIT Dept IT Convergence Engn Gumi South Korea

ISBN: (纸本)9781665423830

The modern era is filled with smart entities (e.g., smart vehicles) that have both sense and actuate capabilities. These entities can collect lots of data during their functional period and these data can be utilized for the wellbeing of citizens. However, these data are very sensitive raising issues like privacy. Moreover, network scarcity, bandwidth consumption, etc. can worsen the circumstance. Federated learning (FL), internet of drones (IoD), and dew computing (DC) are revolutionary technologies that can be engaged to mitigate the aforementioned challenges. An FL-based computing paradigm is initiated over the dew computing to process road-related data to bring efficiency in the applications (e.g., finding parking locations) utilizing IoD. An experimental environment is established containing a traffic dataset as a proof of concept. The experimental results exhibit the feasibility of the proposed scheme.

关键词： Dew computing federated learning internet of drones long short-term memory

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Decorrelation Proportionate Normalized Least Mean M-estimate Algorithm with Variable Step-Size 22

Decorrelation Proportionate Normalized Least Mean M-estimate...

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5th international conference on Signal Processing and Machine Learning, SPML 2022

作者： Huang, Zongxin Yu, Yi Fan, Yongcun Li, Ke School of Information Engineering Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Sichuan Mianyang China

ISBN: (纸本)9781450396912

To improve the performance of the proportionate normalized least mean M-estimate (PNLMM) algorithm, this paper proposes a variable step-size adaptive decorrelation PNLMM (VSS-AD-PNLMM) algorithm. First, an adaptive decorrelation scheme is designed to speed up the algorithm convergence when aiming at highly correlated input signals. Second, by minimizing the a posterior error, we derived a variable step-size mechanism to solve the trade-off between fast convergence rate and low steady-state misadjustment. Computer simulations in impulsive noises are conducted to demonstrate the effectiveness of the proposed algorithm. © 2022 Association for computing Machinery.

关键词： Impulse noise

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