检索结果-内蒙古大学图书馆

2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020

作者： Nguyen, Toan-Van Huynh-The, Thien Shim, Kyusung An, Beongku Hongik University Dept. of Electronics and Computer Engineering in Graduate School Korea Republic of ICT Convergence Research Center Kumoh National Institute of Technology Korea Republic of Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9781728161648

In this paper, we study multi-hop device-to-device (D2D) communications in cognitive Internet-of-Things (IoT) networks, where all D2D devices harvest energy from a power beacon through energy beamforming to transmit their signals to a destination with the existence of a multiple antennas primary receiver. Under Nakagami-m fading channels, the exact and asymptotic expressions for the outage probability (OP) of the considered system are derived then validated by Monte Carlo simulations. We then develop a deep neural network (DNN) for the OP evaluation with a small root-mean-square error. The designed DNN model can predict the OP with high accuracy while it drastically reduces the execution time, expediting a real-time configuration for multi-hop D2D communications in cognitive IoT networks with energy harvesting. © 2020 IEEE.

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

SOLAS: Superpositioning an Optical Lens in Automotive Simulation

arXiv

引用

arXiv 2025年

作者： Jakab, Daniel Barthel, Julian Braun, Alexander Mohandas, Reenu Deegan, Brian Michael Kumbham, Mahendar Molloy, Dara Collins, Fiachra Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy Co. Limerick V94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in creating realistic synthetic data for road traffic scenarios using a video graphics engine. However, a gap exists in modeling realistic optical aberrations associated with cameras in automotive simulation. This paper builds on the concept from existing literature to model optical degradations in simulated environments using the Python-based ray-tracing library KrakenOS. As a novel pipeline, we degrade automotive fisheye simulation using an optical doublet with +/-2◦ Field of View(FOV), introducing realistic optical artifacts into two simulation images from SynWoodscape and Parallel Domain Woodscape. We evaluate KrakenOS by calculating the Root Mean Square Error (RMSE), which averaged around 0.023 across the RGB light spectrum compared to Ansys Zemax OpticStudio, an industrial benchmark for optical design and simulation. Lastly, we measure the image sharpness of the degraded simulation using the ISO12233:2023 Slanted Edge Method and show how both qualitative and measured results indicate the extent of the spatial variation in image sharpness from the periphery to the center of the degradations. © 2025, CC BY.

关键词： Ray tracing

来源：评论

学校读者我要写书评

暂无评论

Secrecy outage performance and deep learning evaluation of multihop energy harvesting IoT networks over Nakagami-m fading channels

Secrecy outage performance and deep learning evaluation of m...

引用

2020 International Symposium on computer, Consumer and Control, IS3C 2020

作者： Nguyen, Toan-Van Tran, Thong-Nhat Shim, Kyusung Huynh-The, Thien An, Beongku Dept. of Electronics and Computer Engineering in Graduate School Hongik University Korea Republic of ICT Convergence Research Center Kumoh National Institute of Technology Korea Republic of Dept. of Software and Communications Engineering Hongik University Korea Republic of

ISBN: (纸本)9781728193625

In this paper, we study the secrecy outage performance of multi-hop energy harvesting Internet-of-Things (IoT) networks, where all IoT devices harvest energy from a power beacon for conveying a confidential message to multiple legitimate users in the presence of an eavesdropper. To enhance the secrecy outage probability (SOP), we propose and analyze the best relay selection (BRE) and best-path selection (BPA) schemes under Nakagami-m fading environments. Based on the analysis results, we develop a deep learning model for the proposed schemes to evaluate the system SOP. Numerical results show that the BPA scheme greatly outperforms the BRE one, showing the efficiency of the best-path selection approach. Moreover, the deep learning model is capable of predicting the SOP of all schemes with high accuracy while it drastically reduces the execution time, arising a real-time configuration for multi-hop energy harvesting IoT networks. © 2020 IEEE

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

arXiv

引用

arXiv 2022年

作者： Xie, Jinheng Xiang, Jianfeng Chen, Junliang Hou, Xianxu Zhao, Xiaodong Shen, Linlin School of Computer Science & Software Engineering Shenzhen University China Dept. of Computer Science Wenzhou-Kean University Wenzhou China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions. In this paper, we propose Contrastive learning for Class-agnostic Activation Map (C2AM) generation only using unlabeled image data, without the involvement of image-level supervision. The core idea comes from the observation that i) semantic information of foreground objects usually differs from their backgrounds;ii) foreground objects with similar appearance or background with similar color/texture have similar representations in the feature space. We form the positive and negative pairs based on the above relations and force the network to disentangle foreground and background with a class-agnostic activation map using a novel contrastive loss. As the network is guided to discriminate cross-image foreground-background, the class-agnostic activation maps learned by our approach generate more complete object regions. We successfully extracted from C2AM class-agnostic object bounding boxes for object localization and background cues to refine CAM generated by classification network for semantic segmentation. Extensive experiments on CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and WSSS can benefit from the proposed C2AM. Code will be available at https://***/CVISZU/CCAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A Pico-Second Timing System on a Chip (SoC) for Magnetostrictive Displacement Sensors (MDSs)

A Pico-Second Timing System on a Chip (SoC) for Magnetostric...

引用

Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia)

作者： Qin Ke Zhouji Du Hang Yu Yan Li Xinchao Zhong Haiquan Li Binjie Ge Daoming Xi Shenzhen iOT Lab of IC Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Computer Science The University of Auckland Auckland Central Auckland New Zealand Runyu Sensor Technology Co. Ltd Xinhui City Jiangmen China Dept. of Electronics and Computer Engineering SHENZHEN MCU-BIT University Shenzhen China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

Based on the Wiedemann effect, Magnetostrietive Displacement Sensors (MDSs) can measure displacement without contact so that it is free of wear. Therefore, compared with other displacement sensors, the MDSs has a longer service life and is more adaptable to harsh environments. The MDSs are getting more attention and are widely used in industry control system. Precision time interval is the key aspect of the MDSs. In this paper, a pico-second timing system on chip (SoC) is designed in a 180 nm CMOS process for the MDSs applications. SoC is proposed for the MDSs applications. The chip integrates a high speed comparator and a time-to-digital converter (TDC) to build a fine time measurement chain. A 32-MHz microcontroller unit (MCU) realized by RISC-V core with two-stage pipeline is realized for system control. Test results show that the comparator temporal noise is around 1 mV and the TDC achieves a resolution of 156 ps.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm 5

A Prediction of Grain Yield Based on Hybrid Intelligent Algo...

引用

5th International Scientific Conference of Al-Khwarizmi Society, FISCAS 2020

作者： Saleh, Ibrahim Ahmed Alawsi, Wasan Abdallah Alsaif, Omar Ibrahim Alsaif, Khalil University of Mosul Software Engineering Dept. Iraq University of Al- Qadisiyah College Science Iraq Northern Technical University Mosul Technical Institute Iraq University of Mosul Computer Science Dept. Iraq

The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it's very efficient and practicable. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin 5

Method for filtering encrypted traffic using a neural networ...

引用

5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Luksha, Ivan Duy Dinh, Truong Karelin, Evgenii Glushakov, Ruslan Kirichek, Ruslan Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint-Petersburg Russia Faculty Information Technology 1 Posts and Telecommunications Institute of Technology Hanoi Viet Nam Saint-Petersburg State Pediatric Medical University Saint-Petersburg Russia

ISBN: (纸本)9781450387347

Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be "transparent"to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out. © 2021 ACM.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks

Improving the Efficiency of a Multistage Evaporator Station ...

引用

International Conference on Modern Electrical and Energy Systems (MEES)

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko det. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine

ISBN: (纸本)9798350346848

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department.

关键词： Productivity Soft sensors Predictive models Solids Sugar industry Data models Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

arXiv

引用

arXiv 2022年

作者： Wang, Xiang Yang, Yimin Guo, Zhichang Zhou, Zhili Liu, Yu Pang, Qixiang Du, Shan The Dept. of Computer Science Lakehead University Thunder BayON Canada The Dept. of electrical and computer engineering Western University LondonON Canada The School of Mathematics Harbin Institute of Technology Hei Long Jiang China The School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China The School of Microelectronics Tianjin University Tianjin China The Okanagan College KelownaBC Canada The Dept. of Comp. Sci. Math Physics & Statistics The University of British Columbia OkanaganBC Canada

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods. © 2022, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Constrained Decomposition Grid Approach to Disassembly Line Balancing Problems

A Constrained Decomposition Grid Approach to Disassembly Lin...

引用

International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： SiQi Lei XiWang Guo MengChu Zhou Jiacun Wang Liang Qi ShuJin Qin Dept. of Computer and Communication Engineering College Liaoning Petrochemical University Fushun China Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Dept. of Computer Science and Software Engineering Wes Long Branch Monmouth University NJ USA Dept. of Computer Intelligent Science & Technology Shandong Univ. of Science and Tech. Qingdao China College of Economics and Management Shangqiu Normal University Shangqiu China

ISBN: (纸本)9781665417532

Environmental damage caused by rapidly increasing end-of-life products can be reduced if they can be properly disassembled and their parts and material can be reused/recycles. Robots can be used to increase the efficiency of a disassembly line. We can classify disassembly operations into non-destructive and destructive ones. This work presents a mathematical model of a disassembly line balancing problem (DLBP) involving both types of disassembly operations. We propose an improved constrained decomposition grid (CDG) approach to solve the model. Four types of products are disassembled by using CDG, multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm II (NSGAII). The results verify CDG’s feasibility, effectiveness and better performance than its two peers’.

关键词： Mechatronics Machine learning algorithms Petri nets Evolutionary computation Machine learning Mathematical models Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：