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检索条件"任意字段=2022 International Conference on Algorithms, Microchips and Network Applications"

共 1155 条记录，以下是81-90 订阅

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Optimal Launch Bound Selection in CPU-GPU Hybrid Graph applications with Deep Learning 13

Optimal Launch Bound Selection in CPU-GPU Hybrid Graph Appli...

引用

IEEE 13th international Green and Sustainable Computing conference (IGSC)

作者： Rafi, Md Erfanul Haque Qasem, Apan Texas State Univ San Marcos TX 78666 USA

ISBN: (数字)9781665465502

ISBN: (纸本)9781665465502

Graph algorithms, which are at heart of emerging computation domains such as machine learning, are notoriously difficult to optimize because of their irregular behavior. The challenges are magnified on current CPU-GPU heterogeneous platforms. In this paper, we study the problem of GPU launch bound configuration in hybrid graph algorithms. We train a multi-objective deep neural network to learn a function that maps input graph characteristics and runtime program behavior to a set of launch bound parameters. When applying launch bounds predicted by our neural network in BFS and SSSP algorithms, we observe as much as 2.76x speedup on certain graph instances and an overall speedup of 1.31 and 1.61, respectively. Similar improvements are seen in energy efficiency of the applications, with an average reduction of 14% in peak power consumption across 20 real-world input graphs. Evaluation of the neural network shows that it is robust and generalizable and yields close to a 90% accuracy on cross-validation.

关键词： machine learning predictive modeling heterogeneous computing

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Pointer network Solution Pool : Combining Pointer networks and Heuristics to Solve TSP Problems 3

Pointer Network Solution Pool : Combining Pointer Networks a...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Shi, Chenxu Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665459112

This paper use pointer networks to improve the quality of initial solutions generation to solve slow convergence problems and the tendency to fall into local optimal solutions when solving path planning problems by the heuristic algorithm. The results show that the convergence speed and the optimisation outcome of the optimised algorithm are improved and can be effectively used to improve the application of heuristic algorithms such as VNS to the travelling salesman problem. © 2022 IEEE.

关键词： Heuristic algorithms

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Intrusion Detection Systems Based on Stacking Ensemble Learning in VANET 5

Intrusion Detection Systems Based on Stacking Ensemble Learn...

引用

5th international conference on Communications, Signal Processing, and their applications (ICCSPA)

作者： Behravan, Mahshid Zhang, Ning Jaekel, Arunita Kneppers, Marc Univ Windsor Windsor ON Canada Telus Communications Inc Edmonton AB Canada

ISBN: (纸本)9781665482370

Vehicular ad-hoc network (VANET) will play an important role in improving driving safety and efficiency in transport systems. As various attacks arise in VANET, it is essential to design mechanisms that can detect these attacks and then mitigate them. In this paper, we make an effort to detect five different position falsification attacks in VANET, including constant attack, constant offset attack, random attack, random offset attack, and eventual stop attack. Two detection systems based on ensemble machine learning algorithms, including stacking ensemble learning algorithms for classification and stacking ensemble learning for neural network, are proposed. We extracted the most important features by performing feature importance techniques. Then, we train the proposed learning algorithms on VeReMi dataset which includes five different position falsification attacks with three traffic densities and three attacker densities. Extensive experimental results are provided to evaluate the proposed solutions' effectiveness. Based on our results, stacking ensemble learning for classification algorithm can achieve the best performance in terms of accuracy and recall. In low density traffic, accuracy and recall of stacking ensemble learning for classification algorithms are 1 for the constant attack, constant offset attack, and random attack. Accuracy and recall for the random offset attack are 0.999 and 0.996, respectively. For the eventual stop attack, accuracy and recall are 0.995 and 0.985, respectively. In medium density, accuracy and recall of stacking ensemble learning also achieve the best performance.

关键词： Intrusion detection system Stacking Ensemble learning VeReMi dataset Stacking for neural network

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Image Style Transfer Using Deep Learning Methods

Image Style Transfer Using Deep Learning Methods

引用

IEEE international conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Ren, Sihan Sheng, Yiwei Nanjing Normal Univ Dept Comp & Elect Informat Nanjing Peoples R China North China Elect Power Univ Dept New Energy Beijing Peoples R China

ISBN: (纸本)9781665416061

Image style transfer is an increasingly popular technology that can learn the style of an existing picture through neural network algorithms and apply this style to another picture. It is widely used in the field of art, such as oil painting, cartoon animation production, image season conversion and text style conversion. Meanwhile, deep learning methods are attracting more and more attention both in research and applications in various areas. In this paper, we give an overview on current research progress and results of image style transfer using deep learning methods. The deep learning methods are categorized into Convolutional Neural networks (CNN) and Generative Adversarial networks (GAN). As for CNN methods, we mainly talk about models based on VGG;and in terms of GAN methods, conditional GAN, Cycle GAN, and cartoon-GAN methods are contained. Finally, we summarized the shortcomings of the current results and the future study direction.

关键词： image style transfer deep learning convolutional neural network generative adversarial network

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Real-time DNN Model Partitioning for QoE Enhancement in Mobile Vision applications 19

Real-time DNN Model Partitioning for QoE Enhancement in Mobi...

引用

19th Annual IEEE international conference on Sensing, Communication, and networking (IEEE SECON)

作者： Lim, Jeong-A Kim, Yeongjin Inha Univ Dept Elect Engn Incheon South Korea

ISBN: (数字)9781665486439

ISBN: (纸本)9781665486439

As deep learning technology advances, mobile vision applications such as augmented reality or autonomous vehicles are widespread. The quality of experience (QoE) of such applications highly depends on hardware specification of mobile device, dynamic service requests, stochastic network status and characteristics of DNN model. In this paper, we propose an algorithm called RT-DMP that jointly optimizes DNN model partitioning and process/network resources adapting to system dynamics by leveraging virtual queue-based Lyapunov optimization framework. The RT-DMP jointly makes decisions on (i) partition point between a mobile device and an MEC server, (ii) mobile GPU clock frequency, and (iii) transmission rate through the wireless network every time slot. We theoretically show that RT-DMP optimally strikes the balance among three QoE metrics that are energy consumption, throughput and end-to-end latency, which has not been addressed in existing studies. Finally, we demonstrate the performance and feasibility of RT-DMP via trace-driven simulations and real testbed based on Nvidia Jetson TX2 and a high-end MEC server.

关键词： Energy consumption Adaptation models Computational modeling Heuristic algorithms Mobile handsets Partitioning algorithms Quality of experience

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Potential applications and Prospects of TV Director Talent Cultivation in the Context of Artificial Intelligence algorithms 24

Potential Applications and Prospects of TV Director Talent C...

引用

5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Liang, Jing Cao, Chang Wang, Zhiguo Xie, Yumeng Li, Hongjie Zhu, Xingyu Network and Educational Technology Center Jinan University Guangdong Guangzhou China Office of Scientific Research and Experimental Development Jinan University Guangdong Guangzhou China

ISBN: (纸本)9798400709999

With the widespread adoption of AI algorithms and the transformative impact of 5G and 4K/8K technologies in media production, the training of television directing talent faces new challenges and opportunities. This study explores the potential applications of AI in practical teaching, particularly in multi-camera switching, real-time image processing, and automated editing. In parallel, the evolution of online live broadcasting, accelerated by post-pandemic media dynamics and heightened viewer expectations, necessitates a sophisticated skill set for TV directors. As crucial figures in live broadcasts, TV directors must navigate these technological advancements and the changing aesthetic landscape. This paper proposes a comprehensive, hybrid teaching model that integrates AI technologies with Electronic Studio Production (ESP) and Electronic Field Production (EFP) methods. By doing so, it aims to enhance students' practical skills, problem-solving abilities, and collaborative capabilities. Our findings highlight the importance of immersive practical training in preparing students for the evolving demands of TV directing in the context of new media. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Television studios

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Reliable network-Packet Binary Classification 4th

Reliable Network-Packet Binary Classification

引用

4th international conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Gudla, Raju Vollala, Satyanarayana Amin, Ruhul Dr SPM Int Inst Informat Technol Dept Comp Sci & Engn Naya Raipur 492002 Chhattisgarh India

ISBN: (纸本)9783031276088;9783031276095

A network packet identification and classification is a fundamental requirement of network management to maintain the quality of service, quality of experience, efficient bandwidth utilization, etc. This becomes increasingly significant in light of the Internet's and online applications' rapid expansion. With the advent of secure applications, more and more encrypted traffic is proliferated on the internet. Specifically, peer-to-peer applications with user-defined protocols severely affect network management. So there is necessary to identify and classify encrypted traffic in a network. To overcome this, our proposed approach network-packet binary classification is implemented to classify the network traffic as encrypted or compressed packets, with better classification accuracy and with the usage of a limited amount of classification time. To achieve this, our model uses a Decision tree classifier with one of the efficient feature selection methods, Autoencoder. Our experimental results show that our model outperforms the most state-of-the-art methods in terms of classification accuracy. Our model achieved 100% classification accuracy within 0.009 s of processing time.

关键词： network packet classification Machine learning algorithms Dimensionality reduction techniques Classification time

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The Role of Synthetic Data in Improving Neural network algorithms 4

The Role of Synthetic Data in Improving Neural Network Algor...

引用

4th international conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Rabchevsky, Andrey N. Yasnitsky, Leonid N. Perm Stane University Mechanical and Mathematical Department Perm Russia

This review article describes synthetic data, its applications, and examples of improving neural network algorithms with synthetic data. Using these examples, we show the important role of synthetic data in the improv... 详细信息

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

关键词： Artificial intelligence

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Ancient Character Image Classification Model Training 3

Ancient Character Image Classification Model Training

引用

3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Lin, Yixuan Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781665459112

Nowadays, various neural network models are updated, and most industries around the world need deep learning algorithms to solve a lot of practical problems. In this paper, we propose the task of image recognition of ancient Chinese characters based on RESNET network model, in order to provide help for students to learn ancient Chinese characters. In the work, the classification of five ancient Chinese characters is completed. The results of RESNET network model are very good, and the accuracy of the final result of the test set is 90%. At the same time, the stability of the model was tested after training, including vertical and horizontal flipping of the image of the test set, and adding noise to the image of the test set. Finally, the RESNET network model is summarized and its applicable environment is described. © 2022 IEEE.

关键词： Neural network models

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An Investigation into the Methods and applications of Deep Learning in Smart Grid 10

An Investigation into the Methods and Applications of Deep L...

引用

10th IEEE Jubilee international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Rituraj, Rituraj Obuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

The manuscript represents the state-of-the-art review of the deep learning methods for smart grid applications. This paper reviews novel applications of deep learning algorithms in smart grid. The deep learning based three algorithms i.e., Long-Short Term Memory, recurrent neural network, convolution neural network found to be most useful in smart grid application. These algorithms are found to be most useful for forecasting, cyber-attack, anomaly detection and electricity theft in smart grid. This paper briefly surveys the most usable deep learning algorithms for making the smart is resilience, accurate, and safe. The review result shows that the mentioned deep learning algorithms give an excellent results over other deep learning algorithm. Therefore, these three algorithms are widely acceptable for the evaluation of smart grids. © 2022 IEEE.

关键词： Long short-term memory

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