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Distracted Driving Detection with Machine Learning Methods by CNN Based Feature Extraction 基于CNN特征提取的机器学习分心驾驶检测方法
Pub Date : 2021-12-31 DOI: 10.18100/ijamec.1035749
Shafeeq Kanaan Shakir AL-DOORI, Yavuz Selim Taspinar, M. Koklu
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引用次数: 11
Classification of left and right hand motor imagery EEG signals by using deep neural networks 基于深度神经网络的左、右手运动意象脑电信号分类
Pub Date : 2021-12-29 DOI: 10.18100/ijamec.995022
Nuri Korhan, Leyla Abi̇lzade, Taner Ölmez, Zümray Ölmez
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引用次数: 1
The Genus-Level Identification of Leaf Beetles (Coleoptera: Chrysomelidae) From Habitus Images with Convolutional Neural Network Classification 用卷积神经网络分类从生境图像中识别叶甲虫属(鞘翅目:金甲科)
Pub Date : 2021-12-29 DOI: 10.18100/ijamec.989263
Mahmut Tokmak, I. Sen
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引用次数: 0
The significance of Enterprise Architecture in driving Digital Transformation on Public sectors 企业架构在推动公共部门数字化转型中的意义
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.949442
Modikwa Luwi Makovhololo, P. Makovhololo, T. Sekgweleo
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引用次数: 1
Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning 利用机器学习预测COVID-19对加密货币供需的影响
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.958160
D. Oyewola, E. Dada, J. Ndunagu, Daniel Eneojo Emmanuel
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引用次数: 0
Departure Passenger Flow Simulation: Case Study on Izmir Adnan Menderes Airport Pre and Post COVID-19 离境客流模拟:以伊兹密尔·阿德南·门德斯机场疫情前后为例
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.928696
S. Ateş, Gökhan Koç, Talha Koç, Mustafa Bolat
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引用次数: 0
Fuzzy Control System for Predicting The Quality of Thermally Processed Raw Milk 热加工原料奶质量预测的模糊控制系统
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.980645
B. Uzun, N. Allahverdi
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引用次数: 0
The effects of geometrical parameters on resonance frequency of Inverted Split Ring Resonators (ISRRs) 几何参数对倒开环谐振器谐振频率的影响
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.879343
I. Yariçi, Y. Ozturk
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引用次数: 0
Reducing Electromagnetic Interference in Three-Level T-type Isolated Bidirectional DC-DC Converter Using a Snubber Circuit 利用缓冲电路降低三电平t型隔离双向DC-DC变换器中的电磁干扰
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.934394
Halime Hizarci, Kemal Kalayci, O. Demirel, U. Arifoglu
Article history: Received 07 May 2021 Accepted 13 August 2021 Electromagnetic interference is the main problem in high switching power converters like DC-DC converters. One of the solution methods for electromagnetic interference reduction is to design a snubber circuit. The snubber circuit is a practical method to reduce fluctuations caused by parasitic components from the layout and non-ideal characteristics of switching devices. An effective snubber circuit design increases the converter's efficiency and reduces EMI noise. In this paper, electromagnetic interference is reduced using different snubber circuits in a three-level T-type isolated bidirectional DC-DC converter. The converter is simulated in PSIM to see the effectiveness of the noise reduction with the snubber circuits. CISPR 25 standard is used for the compliance assessment in the study.
电磁干扰是高开关功率转换器(如DC-DC转换器)的主要问题。减少电磁干扰的解决方法之一是设计缓冲电路。缓冲电路是一种实用的方法,可以减少开关器件的布局和非理想特性所引起的寄生元件波动。有效的缓冲电路设计提高了变换器的效率并降低了电磁干扰噪声。本文在三电平t型隔离型双向DC-DC变换器中采用不同的缓冲电路来减小电磁干扰。在PSIM中对该变换器进行了仿真,验证了缓冲电路的降噪效果。本研究采用CISPR 25标准进行合规性评估。
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引用次数: 1
Smoke detection from foggy environment based on color spaces 基于色彩空间的雾环境烟雾检测
Pub Date : 2021-09-30 DOI: 10.18100/ijamec.973440
M. E. Özbek, Uğur Yıldız
Detection of smoke from videos captured by surveillance cameras in outdoor environments is one of the useful outcome of Internet of Things (IoT) applications. The potential benefit increases when deep learning (DL) architectures are involved. However, an inherent difficulty is to detect smoke while natural events like fog exists. The effectiveness of color spaces in detection performance has not yet fully evaluated in those architectures. Moreover, the energy and memory requirements of DL architectures may not be applicable for handling IoT implementation demands. Therefore, in this work, a DL architecture with a suitable color space model, applicable for IoT implementations is proposed to detect smoke from videos in foggy environment. By collecting several videos including smoke samples, the performance comparison of popular and the state-of-the-art DL architectures denoted the outperforming result according to both accuracy and memory usage.
从室外环境中监控摄像头拍摄的视频中检测烟雾是物联网(IoT)应用的有用成果之一。当涉及深度学习(DL)架构时,潜在的好处会增加。然而,一个固有的困难是在雾等自然事件存在的情况下探测烟雾。在这些体系结构中,色彩空间在检测性能方面的有效性尚未得到充分的评估。此外,DL架构的能量和内存需求可能不适用于处理物联网实现需求。因此,在这项工作中,提出了一种适合物联网实现的具有合适色彩空间模型的深度学习架构来检测雾蒙蒙环境下视频中的烟雾。通过收集包括烟雾样本在内的多个视频,对流行的深度学习架构和最先进的深度学习架构进行性能比较,表明根据准确率和内存使用情况,表现优异的结果。
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引用次数: 1
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International Journal of Applied Mathematics Electronics and Computers
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