2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)最新文献_第4页

Deep Learning Used to Recognition Swimmers Drowning 深度学习用于识别溺水游泳者

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704884

Jiaqing Jian, Chuin-Mu Wang

引用次数: 2

PV System Using Intelligent Controller for Unbalanced Current Compensation 基于智能控制器的光伏系统不平衡电流补偿

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704965

K. Tan, F. Lin

{"title":"PV System Using Intelligent Controller for Unbalanced Current Compensation","authors":"K. Tan, F. Lin","doi":"10.1109/SNPD51163.2021.9704965","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704965","url":null,"abstract":"A novel method is proposed to compensate the three-phase unbalanced currents of power grid under three-phase unbalanced load for a two-stage photovoltaic (PV) power system without the augmentation of active power filter (APF). The PV power system is composed of an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) inverter. Moreover, in the proposed method, dq0-axis compensation currents are obtained through low pass filters (LPFs) to compensate the three-phase unbalanced currents of power grid. Furthermore, to improve the control performance of the DC bus voltage of the PV power system under unbalanced load variation condition, an online trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) is proposed to replace the traditional proportional-integral (PI) controller for the DC bus voltage control. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. In addition, the dimensions of the Gaussian membership functions are directly extended to AMFs. Additionally, the proposed controllers of the PV power system are implemented by two control platforms using floating-point digital signal processor (DSP). Finally, excellent compensation performance for the three-phase currents of power grid under three-phase unbalanced load can be achieved from the experimental results.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125887817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Apply Image Identification to Improve the Localization of the Self-Driving Vehicles 应用图像识别技术提高自动驾驶车辆的定位

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704956

Shaohui Liu, Shih-Yen Huang

引用次数: 0

Truck Driving Assistance System 卡车驾驶辅助系统

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704970

Chi-Chun Chen, Shang-Lin Tien, Yanhui Lin, Chung-Chen Teng, Meng-Hua Yen

引用次数: 0

Security Analyses of an Anonymous Two Factor Authentication Protocol for Roaming Service in Global Mobile Networks 全球移动网络漫游服务匿名双因素认证协议的安全性分析

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704963

Ya-Fen Chang, Huan-Wen Chen, TingMao Chang, W. Tai

引用次数: 0

Early Identification of Active Developers Based on their Past Contributions in OSS Projects 根据过去在OSS项目中的贡献及早识别活跃的开发人员

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704917

Tomoki Koguchi, Akinori Ihara

{"title":"Early Identification of Active Developers Based on their Past Contributions in OSS Projects","authors":"Tomoki Koguchi, Akinori Ihara","doi":"10.1109/SNPD51163.2021.9704917","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704917","url":null,"abstract":"Open Source Software (OSS) developers are free to contribute and free to leave a project, if the project is (not) suitable for them. On the one hand, OSS projects need to manage the human resource to continuously maintain OSS in the future. Some existing studies proposed an approach that estimates how long developers contribute to OSS projects. Using developers’ contributions during the first few months in the target project, the proposed model identified long-term contributors or core developers. However, the approach frequently miss to find capable developers because many developers leave the project soon after participating. To avoid the loss of capable developers, this study build a prediction model to identify future active developers based on their past contributions to any OSS projects. Using dataset from four large-scale OSS projects as a case study, we evaluated our proposed model to identify future active developers based on their past contributions to any OSS projects before participating in a future target project. Our proposed approach contributes to manage human resource in OSS development process.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of the Height of 3D Objects by Moire Measurement 用云纹测量法测定三维物体的高度

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704929

Shang-Ya Wu, Hsia-Ping Lan, Chofan Hsieh, Kao-Chi Lin, Pin-Yu Yeh, Cheng-Yu Peng

引用次数: 0

An Efficient HEVC Intra Frame Coding Based on Deep Convolutional Neural Network 基于深度卷积神经网络的高效HEVC帧内编码

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704928

Tien-Yang Hsu, Yu Lu, Tung-Hung Hsieh, Chou-Chen Wang

{"title":"An Efficient HEVC Intra Frame Coding Based on Deep Convolutional Neural Network","authors":"Tien-Yang Hsu, Yu Lu, Tung-Hung Hsieh, Chou-Chen Wang","doi":"10.1109/SNPD51163.2021.9704928","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704928","url":null,"abstract":"High efficiency video coding (HEVC) is a very popular video coding standard. The HEVC can achieve high coding efficiency with a lower bitrate for intra frame coding. However, it still needs many bits to finish best rate-distortion (R-D) curve. Since there are only 35 directions prediction modes provided in intra prediction module (IPM), HEVC occurs a large distortion when the image contents are out of these prediction directions. In order to obtain a better R-D curve, Zhang et al. [3] recently proposed a simple convolutional neural network (S-CNN) to improve the encoding performance of HEVC. However, S-CNN has to consume more time to encode intra frame coding since it needs to perform more CNN enhancement mode. In order to further speed up S-CNN based intra frame coding, we propose an early termination algorithm to skip CNN. Because the natural images are generally homogenous, we find the mean square errors (MSE) of reconstructed CTU exist high spatial correlation at HEVC encoder. Therefore, a dynamic threshold of MSE is set according to three neighboring encoded CTU blocks to evaluate whether the current reconstructed CTU is useful for the CNN enhancement mode. Simulation results show that the proposed method can achieve faster HEVC encoding process than S-CNN by reducing time increase ratio (TIR) about 12% on an average.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130129697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A YOLO-Based Method for Oblique Car License Plate Detection and Recognition 一种基于yolo的斜车牌检测与识别方法

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9704935

Wei-Chen Li, Ting-Hsuan Hsu, Ke-Nung Huang, Chou-Chen Wang

{"title":"A YOLO-Based Method for Oblique Car License Plate Detection and Recognition","authors":"Wei-Chen Li, Ting-Hsuan Hsu, Ke-Nung Huang, Chou-Chen Wang","doi":"10.1109/SNPD51163.2021.9704935","DOIUrl":"https://doi.org/10.1109/SNPD51163.2021.9704935","url":null,"abstract":"In recent years, automatic license plate recognition (ALPR) system is applied in some traffic-related applications based on deep learning. However, the new ALPR is very difficult to obtain high detection and recognition rates for oblique car license plate (LP). Recently, Silva et al. [5] proposed a warped planar object detection (WPOD) based on deep convolutional neural network (CNN) to overcome the oblique views of LP. Although the WPOD network can achieve the location and rectification of LPs, the loss function of WPOD renders the confidence parameter due to high computational complexity. This also leads to WPOD network cannot locate the optimal LP bounding box. In order to further improve the accuracy of ALPR system, we develop a simple intersection over union (IOU) algorithm to speed up the calculating process of confidence. In this paper, the four-vertex coordinates of the label bounding box and prediction bounding box of oblique LP are used to generate two rectangular boxes, and then a simple IOU algorithm is used to fast calculate the approximate value of IOU. Simulation results show that the proposed ALPR system can arrive a high accuracy of LP recognition about 95.7% on an average. In addition, the proposed system also can achieve higher recognition rate about 1% when compared to the Silva’s ALPR system.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure-Preserving Deep Autoencoder-based Dimensionality Reduction for Data Visualization 基于结构保持深度自编码器的数据可视化降维方法

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI: 10.1109/SNPD51163.2021.9705000

Ayushman Singh, Kaustuv Nag

引用次数: 0