2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第4页

A note on security management of the Internet of Things 关于物联网安全管理的说明

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946616

Long Chen, S. Erfani

引用次数: 8

A comprehensive filter to reduce drift from Euler angles, velocity, and position using an IMU 一个全面的过滤器，以减少漂移欧拉角，速度和位置使用IMU

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946615

S. A. M. Lajimi, J. McPhee

引用次数: 4

Data fusion for fault diagnosis in smart grid power systems 面向智能电网故障诊断的数据融合

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946717

Mojtaba Kordestani, M. Saif

{"title":"Data fusion for fault diagnosis in smart grid power systems","authors":"Mojtaba Kordestani, M. Saif","doi":"10.1109/CCECE.2017.7946717","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946717","url":null,"abstract":"In smart grid power systems, fast and accurate fault detection and diagnosis (FDD) is vital for isolating faulty components and avoiding further complications. This paper introduces a new data fusion method based on ordered weighted averaging (OWA) operator for power smart grids. For this purpose, the discrete time data from circuit breakers (CB) is combined with continuous time data of recorders to enhance the reliability of the fault diagnosis approach. Radial basis functions (RBF) artificial neural network and wavelet transform (WT) are individually employed to identify the location of the fault from the continuous voltage of the buses. Then, a combination of these two methods along with the information from CBs are utilized into a unique framework by OWA operator to diagnose the faults at an early stage. IEEE standard 14 bus system is used to illustrate and validate the proposed method. Several phase to ground faults are injected into the simulation model to validate the diagnostic capability of the FDD system. Simulation results show a better performance of the fusion FDD system in comparison with three other methods.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117243201","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: 22

A CPU-based algorithm for traffic optimization based on sparse convolutional neural networks 基于稀疏卷积神经网络的cpu交通优化算法

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946788

Zhizhou Li, Justin A. Eichel, A. Mishra, Andrew Achkar, Sagar Naik

{"title":"A CPU-based algorithm for traffic optimization based on sparse convolutional neural networks","authors":"Zhizhou Li, Justin A. Eichel, A. Mishra, Andrew Achkar, Sagar Naik","doi":"10.1109/CCECE.2017.7946788","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946788","url":null,"abstract":"Sparsity in the weights of deep convolutional networks presents a tremendous opportunity to reduce computational requirements. In order to optimize flow of traffic systems, any viable solution must be able to operate at real-time. Existing computation frameworks do not yet realize the full potential speedup afforded by sparse neural networks. Meanwhile, the power consumption for a GPU is too great for widely distributed, embedded optimization systems. Here, the authors propose a procedure for realizing the potential of sparse convolutional kernels on CPU. After preprocessing, a code-generator creates well-optimized and deployable code. Measuring the performance of the CPU-mode Tensorflow, the GPU-mode Tensorflow and this proposed solution on two different sparse convolutional neural networks shows that the proposed solution is 2 to 5 times faster than the CPU-mode Tensorflow and costs less power than the GPU-mode Tensorflow. The runtime of the proposed solution is 0.13s per 321 × 321 RGB image on a 98% sparse network, which is 5 times faster than the CPU-mode Tensorflow.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"45 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125823910","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

Decimal floating-point multiplier with binary-decimal compression based fixed-point multiplier 基于二进制-十进制压缩的十进制浮点乘法器

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946692

Shuli Gao, D. Al-Khalili, J. Langlois, N. Chabini

引用次数: 4

Enhancing Suricata intrusion detection system for cyber security in SCADA networks 增强Suricata入侵检测系统在SCADA网络中的网络安全

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946818

Kevin Wong, C. Dillabaugh, N. Seddigh, B. Nandy

引用次数: 34

Optimization of secrecy rate in cooperative device to device communications 设备间协作通信中保密率的优化

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946656

Maryam Alibeigi, Abbas Taherpour, S. Gazor

引用次数: 6

A new realistic optimization-free economic load dispatch method based on maps gathered from sliced fuel-cost curves 基于切片燃料成本曲线图的无优化经济负荷调度新方法

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946626

Ali R. Al-Roomi, M. El-Hawary

{"title":"A new realistic optimization-free economic load dispatch method based on maps gathered from sliced fuel-cost curves","authors":"Ali R. Al-Roomi, M. El-Hawary","doi":"10.1109/CCECE.2017.7946626","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946626","url":null,"abstract":"Many methods have been introduced as economic load dispatchers. However, all these methods depend on the optimization field, where most of these studies mainly focus on how to strengthen the optimization algorithms themselves. This means the mystery key that segregate between the good and bad ELD solvers is the optimization algorithm itself. However, there is a practical fact known in many real power stations that the generators set-points, assigned by automation centers, are actually in discrete form. Thus, the solutions presented in the literature are infeasible if they are seen from this practical point of view. One of the approaches is to use combinational optimization algorithms. Because most of ELD problems have limited number of generating units, so all the solutions can be extracted by slicing the fuel-cost function of each unit in order to have a full map of all feasible solutions. Based on that, the exact optimal solution as well as all other best solutions can be easily obtained by this method. The main challenge that could be faced with this optimization-free method is when the dimension of the given ELD problem is large, because the discrete search space exponentially increases as the number of variables or/and slicing resolution increases. This challenge can be avoided if that ELD problem is correctly formulated based on real configurations of electric power stations. Some numerical experiments are given to illustrate the mechanism of the proposed technique.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130037379","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: 1

Hierarchical cluster-based cooperative spectrum sensing in cognitive radio employing soft-hard combination 基于层次聚类的认知无线电软硬结合协同频谱感知

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946839

Noor Salout, Faroq A. Awin, Abeer F. Alqawasmeh, E. Abdel-Raheem

引用次数: 7

Wearable device for increasing the slow wave sleep stage by electrocutaneous stimulation 通过皮肤电刺激增加慢波睡眠阶段的可穿戴设备

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946639

Francia Dauz, E. Strumban, R. Maev

引用次数: 3