2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)最新文献_第3页

Performance evaluation of Dynamic Neural Networks for mobile radio path loss prediction 动态神经网络在移动无线电路径损耗预测中的性能评价

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894698

A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri

{"title":"Performance evaluation of Dynamic Neural Networks for mobile radio path loss prediction","authors":"A. Bhuvaneshwari, R. Hemalatha, T. Satyasavithri","doi":"10.1109/UPCON.2016.7894698","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894698","url":null,"abstract":"The prediction of path loss for the mobile radio signals is an important part in the design phase of the wireless cellular networks. In the process of modelling the path loss, the GSM 900 MHz signals are collected experimentally using Test Mobile System (TEMS) tool in the dense urban environment of Hyderabad city. In this paper, the best suited Cost 231 Hata empirical propagation model is implemented using three major dynamic neural networks namely, Focused Time Delay Neural Network (FTDNN), Distributed Time Delay Neural Network (DTDNN) which are feed forward dynamic neural networks and Layer Recurrent Neural Network (LRNN) which is a feedback dynamic neural network. The aim of these implementations is to minimise the errors between simulations and measurements. The dynamic neural networks are trained using Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms. Comparisons are made by varying the number of neurons in the hidden layer and changing the training epochs. The performance is analysed in terms of correlation with the measured data, standard deviation, mean error between the targets and outputs and computation times. From the results it is inferred that, the best correlation between simulations and measurements is 0.9972, standard deviation of error (0.04) and mean error (−5.379e-5) are least for Layer Recurrent Neural Network, trained by Levenberg method, but at the cost of increased computation time. With respect to the feed forward dynamic networks, the results show that FTDNN trained by Levenberg algorithm has a better performance compared to DTDNN.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117127704","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: 10

Applying software metrics for the mining of design pattern 应用软件度量来挖掘设计模式

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894692

A. Dwivedi, Anand Tirkey, S. K. Rath

{"title":"Applying software metrics for the mining of design pattern","authors":"A. Dwivedi, Anand Tirkey, S. K. Rath","doi":"10.1109/UPCON.2016.7894692","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894692","url":null,"abstract":"Development of desired software in the present day scenario is becoming too much complex as the user requirements becoming complex day-by-day. Hence there is a need for developing the right methodology for solving complex problem. To solve various problems in design phase, a number of tools and techniques are available and one of them is known as the use of design pattern, which helps to find a better solution for the problems, which are recurring in nature. It is often desired to detect design patterns from the source code of similar category of software, as it improves maintainability of source code of a software. In this study, mining of design pattern technique has been presented, which is based on supervised learning techniques as well as software metrics. During the pattern mining process, metrics-based dataset is developed. Subsequently, machine learning techniques such as Layer Recurrent Neural Network and Random Forest are applied for the pattern mining process. For the critical examination of the proposed study, data from an open source software e.g., JUnit is considered for the mining of software patterns.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104372","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: 7

Unsupervised learning in islanding studies: Applicability study for predictive detection in high solar PV penetration distribution feeders 孤岛研究中的无监督学习:高太阳能光伏渗透配电馈线预测检测的适用性研究

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894680

Shashank Vyas, R. Kumar, R. Kavasseri

{"title":"Unsupervised learning in islanding studies: Applicability study for predictive detection in high solar PV penetration distribution feeders","authors":"Shashank Vyas, R. Kumar, R. Kavasseri","doi":"10.1109/UPCON.2016.7894680","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894680","url":null,"abstract":"Unintentional islanding is a pressing issue associated with integration of distributed solar photovoltaic generation with a distribution network. The probability of its occurrence is usually dominated by the photovoltaic penetration however, as a direct consequence of this, load-inverter dynamic interactions alongside grid-side disturbances can also lead to anomalous instances that can become responsible for accidental island creation and one such anomaly has been described in this work. Given such dynamic behaviours occurring on photovoltaic inverter-integrated distribution feeders, threshold based classical islanding detection can not suffice and hence machine learning based techniques have began to be researched and adopted. However the orientation can be directed towards predictive approaches leveraging knowledge extraction from huge event data available in smart grids. Furthermore, unsupervised learning can be explored for real-time applications to enable self-learning and acting systems. This paper presents preliminary results of application of a self-organizing map neural network for preemptive detection of unintentional islanding by classifying the discovered islanding precursor from other power system events. Classification of a three phase short-circuit fault at the point of common coupling was found to be invariant to input feature reduction however the same gives contrasting results for the other two test cases investigated.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125835588","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: 3

Harmonic mitigation technique for DSTATCOM using continuous time LMS adaptive filter 基于连续时间LMS自适应滤波器的DSTATCOM谐波抑制技术

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894617

S. K. Patel, S. Arya, R. Maurya

引用次数: 4

An approach to data recording and management in airborne radar 机载雷达数据记录与管理方法

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894660

D. Deb, Reena Mamgain

引用次数: 0

Design of PV powered SR motor driven irrigation pumps utilizing boost converter 利用升压转换器的光伏驱动SR电机驱动灌溉泵的设计

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894663

A. Mishra, Bhim Singh

{"title":"Design of PV powered SR motor driven irrigation pumps utilizing boost converter","authors":"A. Mishra, Bhim Singh","doi":"10.1109/UPCON.2016.7894663","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894663","url":null,"abstract":"This paper presents the design of a cost-effective and efficient solar powered irrigation pump utilizing a switched reluctance motor (SRM). It utilizes a simple DC-DC boost converter as a power conditioning stage between SPV array and the motor drive. The control of solar photovoltaic (SPV) array output power at maximum power point (MPP) and facilitating the soft-starting to the SRM drive, are two prime functions of the boost converter. The use of a 4-phase SRM drive minimizes the torque ripple and increases the number of strokes without incrementing the number of rotor poles. The low number of switches in a mid-point converter used to energize SRM phases further enhances the performance of the system. The speed control of the motor using the pulse width modulation (PWM) switching of split-capacitor converter eliminates the requirement of additional sensors on the motor to control its speed. The proposed water pumping system is designed, modeled and its performance is simulated on MATLAB/Simulink platform and its responses are analyzed, under the varying environmental conditions, which authenticate its appropriateness as an irrigation pump.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115156787","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: 7

Automation of solar system for Maximum Power Point tracking using Artificial Neural Networks and IoT 利用人工神经网络和物联网实现太阳能系统最大功率点跟踪的自动化

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894625

K. Sujatha, R. S. Ponmagal, T. Godhavari, K. Kumar

引用次数: 8

Video Affective Content Analysis based on multimodal features using a novel hybrid SVM-RBM classifier 基于多模态特征的视频情感内容分析与SVM-RBM混合分类器

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894690

Ashwin T S, Sai Saran, G. R. M. Reddy

{"title":"Video Affective Content Analysis based on multimodal features using a novel hybrid SVM-RBM classifier","authors":"Ashwin T S, Sai Saran, G. R. M. Reddy","doi":"10.1109/UPCON.2016.7894690","DOIUrl":"https://doi.org/10.1109/UPCON.2016.7894690","url":null,"abstract":"Video Affective Content Analysis is an active research area in computer vision. Live Streaming video has become one of the modes of communication in the recent decade. Hence video affect content analysis plays a vital role. Existing works on video affective content analysis are more focused on predicting the current state of the users using either of the visual or the acoustic features. In this paper, we propose a novel hybrid SVM-RBM classifier which recognizes the emotion for both live streaming video and stored video data using audio-visual features; thus recognizes the users' mood based on categorical emotion descriptors. The proposed method is experimented for human emotions recognition for live streaming data using the devices such as Microsoft Kinect and Web Cam. Further we tested and validated using standard datasets like HUMANE and SAVEE. Classification of emotion is performed for both acoustic and visual data using Restricted Boltzmann Machine (RBM) and Support Vector Machine (SVM). It is observed that SVM-RBM classifier outperforms RBM and SVM for annotated datasets.","PeriodicalId":151809,"journal":{"name":"2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132337612","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: 7

Performance analysis of four level NPC and NNPC inverters using capacitor voltage balancing method 采用电容电压平衡法分析四电平NPC和NNPC逆变器的性能

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894654

N. Susheela, P. Kumar, C. H. Reddy

引用次数: 9

Model Reference Adaptive System using Rotor Flux and Back Emf techniques for speed estimation of an Induction Motor operated in Vector Control mode: A comparative study 模型参考自适应系统使用转子磁链和反电动势技术估计异步电动机在矢量控制模式下的速度:比较研究

2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) Pub Date : 1900-01-01 DOI: 10.1109/UPCON.2016.7894622

M. Munshi, S. Choudhuri

引用次数: 5