2020 7th International Conference on Electrical and Electronics Engineering (ICEEE)最新文献

Ridge Gap Waveguide Wideband Hybrid Directional Coupler for Ka-Band Applications 用于ka波段的脊隙波导宽带混合定向耦合器

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102609

Mohamed Y. Soliman, M. Ali, S. Shams, M. F. A. Sree, D. Fawzy, A. Allam

{"title":"Ridge Gap Waveguide Wideband Hybrid Directional Coupler for Ka-Band Applications","authors":"Mohamed Y. Soliman, M. Ali, S. Shams, M. F. A. Sree, D. Fawzy, A. Allam","doi":"10.1109/ICEEE49618.2020.9102609","DOIUrl":"https://doi.org/10.1109/ICEEE49618.2020.9102609","url":null,"abstract":"The fifth generation (5G) is the next wireless mobile communication technology that targets to provide huge capac-ity, low latency and high data speeds. Like any new wireless technology, 5G requires the development of exciting techniques, where access to high frequency spectrum can achieve the desired characteristics. In addition, new systems must be developed and designed using wide bandwidth microwave components, which should be implemented with modern and efficient guiding structures. Among all the microwave components, directional coupler is essential device as it can be used to realize many subsystems such as beamforming networks. In order to keep up with the huge changes in future technologies, couplers still requires noticeable improvement, where wide bandwidth and low loss should be achieved throughout using modern guiding technology such as Ridge Gap Waveguide (RGW). In this paper, a design of ultra-wideband 3 dB quadrature hybrid directional coupler using RGW technology is introduced. The proposed coupler has a compact size of $1.58 lambdatimes 1.4lambda$ and achieves a relative bandwidth of 44.7% at 33.5 GHz, which cover the whole ka-band. In addition, a stable 90° phase difference between the coupling and transmission coefficients is obtained over the whole frequency band.","PeriodicalId":131382,"journal":{"name":"2020 7th International Conference on Electrical and Electronics Engineering (ICEEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115481341","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: 5

Approximate Temporal Conditioned Autoencoder and Regressor for Sales Prediction 销售预测的近似时间条件自编码器和回归量

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102518

A. Hashi, Yakup Genç

{"title":"Approximate Temporal Conditioned Autoencoder and Regressor for Sales Prediction","authors":"A. Hashi, Yakup Genç","doi":"10.1109/ICEEE49618.2020.9102518","DOIUrl":"https://doi.org/10.1109/ICEEE49618.2020.9102518","url":null,"abstract":"Lost sales can be caused by out-of-stock products, bad customer-care and over-pricing and is a major source of business failure in retail market. Predicting demand for products and customers' purchasing behaviour can help the businesses avoid these type of failures. Such predictions are usualy done using matrix factorization techniques. These are not suited to handle temporal variations in data which is often the case in retail world. Beyond factorization, deep learning methods including autoencoders and regressor are proposed without considering temporal information. Extending them with deep learning components to handle temporal data is not straightforward as these type of models requires a lot of data with variations in items and customers as well as in time. Variations in item and customer is easily achieved but variation in time is usually limited. We therefore propose a method which approximates a recurrent neural network to handle a few samples in temporal dimension. To this end we start with an existing algorithm DCAR [1] and enhance it to handle a few samples of temporal data resulting a predictor that beats the performance of classical matrix factorization and deep factorization methods as well as DCAR. The method simultaneously trains multiple autoencoders and regressors with an unfolded recurrent neural network.","PeriodicalId":131382,"journal":{"name":"2020 7th International Conference on Electrical and Electronics Engineering (ICEEE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114412476","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

Detailed Design of Signal Conditioning Circuits and Data Acquisition Channel of a Charged Particle Monitor 带电粒子监测仪信号调理电路及数据采集通道的详细设计

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102504

Shubham Sahasrabudhe, Sourabh Nakade, Utsav Singh, Sheetal Lokhande, Daksha Kasliwal, Arunimaa Banerji

引用次数: 1

Optimal Allocation of Energy Storage System in Transmission System Considering Wind Power 考虑风电的输电系统储能系统优化配置

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102603

Ahmad AL Ahmad, R. Sirjani

{"title":"Optimal Allocation of Energy Storage System in Transmission System Considering Wind Power","authors":"Ahmad AL Ahmad, R. Sirjani","doi":"10.1109/ICEEE49618.2020.9102603","DOIUrl":"https://doi.org/10.1109/ICEEE49618.2020.9102603","url":null,"abstract":"Wind power uncertainties should be considered in power system operation and planning. Energy storage system (ESS) can facilitate wind power integration in the energy system. However, maximum benefits can be achieved by optimal determination of the location and sizing of ESSs. In this paper, five-point estimation method is utilized to discretise the wind power distribution into five discrete distributions. Combining the discretizing method with a multi-objective hybrid particle swarm optimisation (MOPSO) and non-dominated sorting genetic algorithm (NSGAII), a hybrid probabilistic optimisation algorithm is constructed. The hybrid algorithm aims to search for the best site and size of energy storage system (ESSs) and considers the power uncertainties of wind farm. System's total expected cost restricted by investment budget, total expected voltage deviation and total expected carbon emission are the objective functions to be minimised. IEEE 30-bus system is adopted to perform the case studies using the hybrid algorithm. The simulation results demonstrate the effectiveness of the hybrid method in solving the optimal allocation problem of ESSs and considering the uncertainties of wind farms' output power.","PeriodicalId":131382,"journal":{"name":"2020 7th International Conference on Electrical and Electronics Engineering (ICEEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123613458","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: 9

Using Buck-Boost Rectifier with Single-Phase-Matrix Converter to Reduce Switch Count Operation 采用Buck-Boost整流器和单相矩阵变换器减少开关计数操作

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102479

Merve Boydak, A. Orhan, A. Caliskan

引用次数: 5

Investigation of Harmonic Frequency Multiplication on Transmitted Data through Pulse Shaping for 6G Communication 6G通信中脉冲整形传输数据的谐波倍频研究

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102565

Özgün Ersoy, A. Sahin

引用次数: 2

Computation of Stabilizing Decentralized PI Controllers for TITO Systems with Simplified and Inverted Decoupling 简化逆解耦TITO系统分散PI控制器的镇定计算

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102547

Miray Günay Bulut, F. N. Deniz

引用次数: 4

Approach for Determination of the Stop Band for Ridge Gap Waveguide 岭隙波导阻带的确定方法

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102494

M. El-Din, H. El-Hennawy, A. Allam, S. Shams, M. F. A. Sree, A. Gaafar

引用次数: 2

Elimination of the Influence of the Light Source on the Multimedia Image 消除光源对多媒体图像的影响

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102507

V. Pyliavskyi

引用次数: 0

MMC-MTDC System Based on Improved Voltage Control Analysis of DC Fault Characteristics 基于改进电压控制的MMC-MTDC系统直流故障特征分析

2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) Pub Date : 2020-04-01 DOI: 10.1109/ICEEE49618.2020.9102513

Zheng Sun, Bin Wang, Yong Yan, Jiang-hua Liu

引用次数: 0