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EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference 23

EEAI: An End-edge Architecture for Accelerating Deep Neural ...

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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： Liu, Guozhi Dai, Fei Huang, Bi Qiang, Zhenping Li, LeCheng Wang, Shuai School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781665494571

Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI) applications in the 5G era, have been widely used in the field of mobile intelligence. However, it is challenging to run computation-intensive DNN-based applications on mobile devices due to the limited resources. What's worse, traditional cloud-assisted approach requires significant amounts of data to be uploaded to the cloud through wireless network, leading to poor real-time performance and low quality of user experience. To address these challenges, in this paper, we propose EEAI, a framework that end-edge collaborative DNN inference. Firstly, we analyze the characteristics of DNN-based applications. Then, we introduce edge computing, and design EEAI, a light-weight scheduler to automatically partition DNN computation between mobile devices and edge servers at the granularity of DNN layers. Finally, we verify the effectiveness of the EEAI under different network environments (4G, 5G, and Wi-Fi). Experiments show that the proposed method is effective and has good performance. © 2021 IEEE.

关键词： Deep learning Performance evaluation Energy consumption 5G mobile communication Neural networks Collaboration Computer architecture

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intelligent Live Video Streaming for Object Detection 23

Intelligent Live Video Streaming for Object Detection

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作者： Chen, Mingkang Sun, Jingtao Aida, Kento Figueiredo, Renato J. Ku, Yun-Jung Subratie, Kensworth The Graduate University for Advanced Studies Sokendai Department of Informatics Tokyo Japan National Institute of Informatics Tokyo Japan University of Florida Electrical and Computer Engineering Florida United States

ISBN: (纸本)9781665494571

These days, sensors and cameras are being deployed on an increasingly large scale. Furthermore, the rapid development of machine learning models for computer vision now presents novel opportunities for the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in various application scenarios. However, challenges remain in supporting low-latency video streaming from distributed mobile IoT devices under dynamic network environments, and overcoming video data quality degradation that results from weather 'noise', which reduces the accuracy of AI-based data analyses such as object detection. In this paper, we propose a live video stream processing system for supporting intelligent services that integrates the following features. First, to cope with dynamic networks and achieve low latency, our approach employs a peer-to-peer (P2P)-based virtual network at the edge and a multi-tiered architecture composed of IoT cameras, edge, and cloud servers. Second, we construct a flexible messaging system for video analysis built upon SINETStream, which is a messaging system that adopts a topic-based pub/sub model. Third, we implement a framework that can remove weather-related (rain, snow, and fog) noise by applying weather classification and adaptive noise removal models that improve the accuracy of video analysis from data collected outdoors. The latency, throughput, and image quality benchmark experiments conducted to validate the feasibility of our proposed system showed that the process resulted in image quality improvements of approximately 30% (on average). © 2021 IEEE.

关键词： Image quality Adaptation models Image edge detection Object detection Streaming media Throughput Internet of Things

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28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

引用

28th international conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on Information and Knowledge engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on Image Processing, Computer Vision, and Pattern Recognition. The topics include: Image-Based Seal Recognition: Approaches and Challenges in Current automated Systems;Deep learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques;using Linkage Context for automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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The Impact of Parameters on Semantic Segmentation: A Case Study on the CamVid dataset 23

The Impact of Parameters on Semantic Segmentation: A Case St...

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作者： Zhang, Yue Yang, Ruiqi Wang, Jun Chen, Nan Dai, Qinling Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Art and Design College Southwest Forestry University Swfu Yunnan Kunming China

ISBN: (纸本)9781665494571

Image segmentation is an important topic of remote sensing image processing. High-resolution remote sensing images contain complex object information. The application of traditional segmentation methods is greatly restricted. Deep learning is suitable for processing remote sensing images due to the complex multi-layer nonlinear transformation characteristics. Many image semantic segmentation methods based on deep learning were proposed, which realizes an end-to-end semantic segmentation workflow. Most of those studies focused on the design of network architectures. In contrast, the impact of parameters on final segmentation accuracy was not fully exploited. In this context, with the CamVid dataset and popular U-Net convolutional neural network and DeeplabV3+, this paper explored the impact of the max Epoch and the initial learning rate on the segmentation results. The study results show that in Deeplab V3+, when the MaxEpochs parameters are different and the InitiallearningRate parameters are the same, the improvement of Global Accuracy is not obvious. When the InitiallearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy has been greatly improved, increasing by about 3%. In U-Net, when the MaxEpochs parameters are different and the InitiallearningRate parameters are the same, the Global Accuracy is improved by about 1%. When the InitiallearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy is greatly improved, increasing by 24%. They can be concluded that the max epochs have slight impact on the final global accuracy, in contrast, the InitiallearningRate has a great impact on the experimental results. © 2021 IEEE.

关键词： Deep learning Image segmentation Smart cities Semantics Network architecture Convolutional neural networks Remote sensing

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Application of Deep learning to the Establishment of Slope Failure Warning Areas

Application of Deep Learning to the Establishment of Slope F...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Rena Tani Michiyuki Hirokane Kazumasa Kuramoto Graduate School of Informatics Kansai University Osaka Japan Faculty of Informatics Kansai University Osaka Japan Chuden Engineering Consultants Co. Ltd. Hiroshima Japan

This study uses a deep learning technique to output the slope failure warning areas from numerical elevation data. This is treated as a semantic segmentation task, with UNet as the network model. The experiment results are compared for cases in which the loss function is used and in which other modifications are made to incorporate the idea of disaster countermeasures.

关键词： Deep learning Training Semantic segmentation data preprocessing Training data Reproducibility of results Numerical models

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Car-Following Model of ULVs using a Deep learning Model

Car-Following Model of ULVs using a Deep Learning Model

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Hiroaki Inokuchi Takamasa Akiyama Department of Civil Environmental and Applied Systems Engineering Kansai University Suita Japan

In recent years, various types of vehicles have been developed and made available for the road. This study clarifies the driving characteristics of Ultra Lightweight Vehicles (ULVs) that consume less energy. First, coordinate data in units of 0.1 seconds are created by observing traffic flows including ULVs using unmanned aerial vehicles (UAVs). In addition, a car-following model is developed using the long short-term memory (LSTM) model, which has attracted attention as a deep learning model. Consequently, a model with a higher degree of fitness than a non-linear model is developed.

关键词： Deep learning Roads Autonomous aerial vehicles Long short term memory

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Comparison of Supervised learning Models for COVID-19 Confirmed Cases Prediction using Correlation Analysis

Comparison of Supervised Learning Models for COVID-19 Confir...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Jun-Su Kim Byung-Jae Choi Department of IT Convergence Engineering Daegu University Gyeongsan Gyeongsangbuk-do South Korea Department of Electronic Engineering Daegu University Gyeongsan Gyeongsangbuk-do South Korea

The COVID-19 virus, which first appeared in 2019, has a strong contagious power and is highly spread by people’s mobility. In this study, correlation analysis is used in statistical preprocessing of dataset which further used to predict the COVID-19 confirmed cases for next day. data is divided into two sets by organizing the data set by data preprocessing using correlation analysis. The first dataset is Google Mobility data of COVID-19 infection with six variables. The second dataset is Google Mobility data of COVID-19 infection with two variables: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The results of predicting the number of confirmed cases are compared using four supervised machine learning models. Furthermore, the soft voting method is used to show more improved results than the individual performances of each method.

关键词： COVID-19 Analytical models Correlation Supervised learning data preprocessing Predictive models data models

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A Model to Automatically Evaluate Beginners’ Programs Based on Syntactic Knowledge Point and Deep learning Technology

A Model to Automatically Evaluate Beginners’ Programs Based...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yu Yan Hirokuni Kurokawa College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Personnel and Training Division Intelligent Vision & Image System. Co. Ltd Tokyo Japan

This report provided a CNN model to automatically evaluate beginners’ programs based on their understanding of program concepts. The model input is a beginner’s program and the output is a letter grade for each program. To train the model, each input program was encoded as a matrix, composed of positive integers by Syntactic Knowledge Point (SKP) technology. Labels for the training data were made by manually evaluating each program in the database, based on the beginner’s understanding of each of the 42 program concepts. In addition, several other technologies such as supervised learning, data standardization, data augmentation have also been used to train the model. Finally, a model with an accuracy of 0.93 has been produced, setting a new benchmark in this young field.

关键词： databases Computational modeling Education Supervised learning Training data Standardization Syntactics

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A Study on CNN Model with Multiple Inputs for Detection of Driving Area

A Study on CNN Model with Multiple Inputs for Detection of D...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Min-Hong Park Jae-Hoon Cho Yong-Tea Kim Department of ICT Robot and Mechanical Engineering Hankyong National Univercity Anseong Gyeonggi Korea Hankyong National Univercity Smart Logistics Technology Institute Anseong Gyeonggi Korea School of ICT Robot and Mechanical Engineering Hankyong National Univercity Anseong Gyeonggi Korea

In this paper, we propose a method to distinguish the drivable and non-drivable areas of a mobile robot by learning road driving image data of an autonomous vehicle. Through recognition of the drivable area of vehicles and mobile robots, it is intended to improve performance more stably when driving and mapping. We propose a CNN model for segmenting the driving area by learning road driving image data. The proposed model proceeds with a performance evaluation of the model through one input and multiple inputs. We shows that the proposed model preserves spatial information and reduces the network parameters.

关键词： Performance evaluation Image segmentation Laser radar Roads Robot sensing systems Cameras data models

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Reducing computational cost in federated ensemble learning via rank-one matrix

Reducing computational cost in federated ensemble learning v...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： YongHoon Kang HoSeung Kim Jee-Hyong Lee Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea Department of Artificial Intelligence Sungkyunkwan University Suwon Republic of Korea

As distributed environments have developed and data privacy has become more important, federated learning attracts more attentions. Federated learning is the method to train deep learning models without data exchange in distributed environments. However, in a Non-Independent and Identically Distributed data environment, performance degradation occurs in the federated learning environment. We want solve this problem by federated ensemble learning but computational cost problem occurs in edge devices. To solve this problem, we proposes FedRE. FedRE reduces computational cost on ensemble model via rank-one matrix and using shared weights to minimize performance degradation when Non-IID situations. As a result of the experiments, SVHN and CIFAR-10 image classification tasks showed high accuracy compared to the previous federated learning methods.

关键词： Performance evaluation Degradation Federated learning Computational modeling Image edge detection Distributed databases Computational efficiency

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