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The Simulation and Design of Zero-Energy Homes—A Comparison between Photo-Voltaic, Solar-Thermal, and Photo-Voltaic-Thermal Systems 零能耗住宅的模拟与设计——光伏、太阳能热、光电热系统的比较
Pub Date : 2022-04-28 DOI: 10.3390/engproc2021012110
M. Ehsan, Zuha Maryam
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引用次数: 0
Rotten-Fruit-Sorting Robotic Arm: (Design of Low Complexity CNN for Embedded System) 腐烂水果分拣机械臂:(面向嵌入式系统的低复杂度CNN设计)
Pub Date : 2022-04-01 DOI: 10.3390/engproc2021012109
M. Amin, Muhammad Hafeez, Qasim Awais
: Industrial Automation has revolutionized the processing industry due to its high accuracy, the time it saves, and its ability to work without tiring. Being the most fundamental part of automation machines, robotic arms are being used as a fundamental component in many types of domestic as well as commercial automation units. In this paper, we proposed a low-complexity convolutional neural network (CNN) model and successfully deployed it on a locally generated robotic arm with the help of a Raspberry Pi 4 module. The designed robotic arm can detect, locate, and classify (based on fresh or rotten) between three species of Mangos (Ataulfo, Alphonso, and Keitt), on a conveyor belt. We generated a dataset of about 6000 images and trained a three-convolutional-layer-based CNN. Training and testing of the network were carried out with MatLab, and the weighted network was deployed to an embedded environment (Raspberry Pi 4 module) for real-time classification. We reported a classification accuracy of 98.08% in the detection of fresh mangos and 95.75% in the detection of rotten mangos. For the designed robotic art, the achieved angle accuracy was 93.94% with a minor error of only 2 ◦ . The proposed model can be deployed in many food- or object-sorting industries as an edge computing application of deep learning.
工业自动化由于其高精度,节省时间和工作不累的能力而彻底改变了加工行业。机械臂作为自动化机器最基本的组成部分,在许多类型的家用和商用自动化装置中被用作基本部件。在本文中,我们提出了一种低复杂度卷积神经网络(CNN)模型,并在树莓派4模块的帮助下成功地将其部署在局部生成的机械臂上。设计的机械臂可以在传送带上对三种芒果(Ataulfo, Alphonso和Keitt)进行检测,定位和分类(基于新鲜或腐烂)。我们生成了大约6000张图像的数据集,并训练了一个基于三卷积层的CNN。利用MatLab对网络进行训练和测试,并将加权网络部署到嵌入式环境(树莓派4模块)中进行实时分类。我们报道了新鲜芒果的分类准确率为98.08%,腐烂芒果的分类准确率为95.75%。对于设计的机器人艺术,实现的角度精度为93.94%,小误差仅为2◦。所提出的模型可以作为深度学习的边缘计算应用程序部署在许多食品或对象分类行业。
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引用次数: 0
Studies on Ceramic Wastes based Composites for Capacitors Applications 陶瓷废料基电容器复合材料的研究
Pub Date : 2022-03-29 DOI: 10.3390/engproc2021012108
M. Sohail, Muhammad Omer, Adnan, Mai G Sayed, A. Shahzad, Ihsan Ullah, Aziz Ahmad
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引用次数: 0
A Modified Battery Charger with Power Factor Correction for Plug-In Electrical Vehicles 一种带有功率因数校正的插电式电动车改进型电池充电器
Pub Date : 2022-03-09 DOI: 10.3390/engproc2021012103
Arfan Tariq
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引用次数: 2
Performance Analysis of Mixed Mode Solar Dryer for Tobacco Leaves 混合模式太阳能烟叶干燥机性能分析
Pub Date : 2022-03-07 DOI: 10.3390/engproc2021012102
Syed Ali Wahaj Abdi, Naveed Ahmad
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引用次数: 1
Model Based Transient Analysis of a Solar Assisted Absorption System for Multi-Climate Zones with Dynamic Building Load 基于模型的多气候带建筑动态负荷太阳能辅助吸收系统暂态分析
Pub Date : 2022-02-15 DOI: 10.3390/engproc2021012100
Atiya Sadiq, Muzaffar Ali
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引用次数: 0
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The 1st International Conference on Energy, Power and Environment
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