2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)最新文献_第2页

Design and Analysis of Fractional Order PID Controller tuning via Genetic Algorithm for CUK Converter 基于遗传算法的CUK变换器分数阶PID控制器整定设计与分析

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721419

Shipra Tiwari, Zeeshan Rayeen, Omar Hanif

{"title":"Design and Analysis of Fractional Order PID Controller tuning via Genetic Algorithm for CUK Converter","authors":"Shipra Tiwari, Zeeshan Rayeen, Omar Hanif","doi":"10.1109/ICIINFS.2018.8721419","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721419","url":null,"abstract":"This paper uses a non-isolated DC-DC Cuk converter, models it into a fourth order differential equation. It further concentrates on identifying the transfer function of the DC-DC Cuk converter. The non-isolated model generates a nonlinear dynamics in its characteristics which is linearized by using the average modelling and small signal analysis is done. Thereafter, the work focuses on designing a Fractional order Proportional Integral Derivative (FOPID) controller using the Genetic Algorithm (GA) minimizing the weighted sum of the error specifications i.e. Integral Time Absolute Error (ITAE), Integral Absolute Error (IAE) and Integral Square Error (ISE). The FOPID controller has more parameters giving the controller more degrees of freedom and accuracy than the classical Proportional Integral Derivative Controller (PID). Henceforth, the work implements the designed controller to the model and records various simulation results like step response, current across inductor, voltage across capacitor, response to a disturbance etc., along with setpoint tracking, disturbance rejection and relative stability analysis.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133685008","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

Performance Comparison of Machine Learning and Deep Learning While Classifying Driver’s Cognitive State 机器学习与深度学习在驾驶员认知状态分类中的性能比较

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721374

R. Bhardwaj, S. Parameswaran, V. Balasubramanian

{"title":"Performance Comparison of Machine Learning and Deep Learning While Classifying Driver’s Cognitive State","authors":"R. Bhardwaj, S. Parameswaran, V. Balasubramanian","doi":"10.1109/ICIINFS.2018.8721374","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721374","url":null,"abstract":"Driver fatigue is a major cause of the road accidents that occur throughout the globe. It has been observed that among total number of accidents, 20% are contributed from driver fatigue. Acknowledging the existing data it is clear that a notification system for driver fatigue is of at most importance. Over the past a large number of strategies have been tested out and among them EEG based systems have shown to be the most accurate and reliable to estimate driver’s cognitive state. The direct relation of brain activity to EEG signal explains its high accuracy in a fatigue detection system. Current researches in machine learning as well as deep learning have shown a new perspective in EEG data analysis. This work proposed a highly accurate, EEG based driver fatigue classification system which can reduce the rate of fatigue related road accidents using machine learning and deep learning algorithms. The results showed that the relative power of theta, alpha, beta and delta showed significant correlation to driver fatigue. The selected features were trained and evaluated using 20 well established classifiers in the field of driver fatigue. Among all the classifiers tested, the Fine Tree, Subspace KNN, Fine Gaussian SVM, and Weighted KNN were performed to the highest accuracy levels. Different performance metrics are used for this work and Deep Autoencoder and KNN are identified as the best suitable Deep learning and Machine Learning Algorithms for driver fatigue prediction with an accuracy of 99.7% and 99.6 % respectively.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654663","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: 6

Input Fusion of MFCC and SCMC Features for Acoustic Scene Classification using DNN 基于深度神经网络的MFCC和SCMC特征输入融合声学场景分类

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721416

Chandrasekhar Paseddula, S. Gangashetty

引用次数: 0

Tracking and Erosion Resistance of Artificially Aged Silicone Rubber Samples 人工老化硅橡胶样品的跟踪和耐侵蚀性能

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721394

A. Verma, B. S. Reddy, S. Bandyopadhyay

引用次数: 1

Deep GoogLeNet Features for Visual Object Tracking 深度GoogLeNet功能的视觉对象跟踪

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721317

P. Aswathy, Siddhartha, Deepak Mishra

{"title":"Deep GoogLeNet Features for Visual Object Tracking","authors":"P. Aswathy, Siddhartha, Deepak Mishra","doi":"10.1109/ICIINFS.2018.8721317","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721317","url":null,"abstract":"Convolutional Neural Network (CNN) has recently become very popular in visual object tracking due to their strong feature representation capabilities. Almost all of the CNN based trackers currently use the features extracted from shallow convolutional layers of VGGNet architecture. This paper presents an investigation of the impact of deep convolutional layer features in an object tracking framework. In this study, we demonstrate for the first time, the viability of features extracted from deep layers of GoogLeNet CNN architecture for the purpose of object tracking. We integrated GoogLeNet features in a discriminative correlation filter based tracking framework. Our experimental results show that the GoogLeNet features provides significant computational advantages over the conventionally used VGGNet features, without much compromise on the tracking performance. It was observed that features obtained from inception modules of GoogLeNet have high depths. Further, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the extracted features. This greatly reduces the computational cost and thus improve the speed of the tracking process. Extensive evaluation have been performed on three benchmark datasets: OTB, ALOV300++ and VOT2016 datasets and its performances are measured in terms of metrics like F-score, One Pass Evaluation, robustness and accuracy.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132294453","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: 19

Privacy Preserving Approach for Association Rule Mining in Horizontally Partitioned Data using MFI and Shamir’s Secret Sharing 基于MFI和Shamir秘密共享的水平分区数据关联规则挖掘隐私保护方法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721388

Nikunj H. Domadiya, U. P. Rao

引用次数: 3

Over-Modulated Reference for 12-Sided Polygonal Space Vector Strategy Based Modified Pulse Pattern for CHB MLI 基于改进脉冲模式的12边多边形空间矢量策略的CHB MLI过调参考

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721371

R. Jonnala, Jyothi Gidda

{"title":"Over-Modulated Reference for 12-Sided Polygonal Space Vector Strategy Based Modified Pulse Pattern for CHB MLI","authors":"R. Jonnala, Jyothi Gidda","doi":"10.1109/ICIINFS.2018.8721371","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721371","url":null,"abstract":"In this paper, a new pulse pattern is designed for multilevel dodecagonal space vector modulation with over modulated reference. This 12-sided polygonal or Dodecagonal Space Vector Modulation is a sub-sectional strategy for the Hexagonal Space Vector Modulation. In this Hexagonal Space Vector Modulation, the pulse durations are calculated based on present position and magnitude of reference. For the proper sharing of present and future sectors in the duration of pulses are decides with reference position and magnitude. But over modulated reference gives a single and specified level throughout the sector duration because reference crosses the space hexagonal region. Over modulating reference simplifies the computational complexity of pulse time calculations in hexagonal space vector, the same strategy is implemented and analyzed for eliminating harmonic distortions in output waveform with sinusoidal approximation in 12-sided polygonal modulation. The proposed over modulated reference implementation eliminates the present position estimation, calculations for sharing of present and future sectors and repetitive pulse generation. The over modulating reference implementation is done with 12-sided polygonal space vector modulation for 7-level cascaded multilevel inverter. Detailed simulation and experimental results for proposed over modulating reference based 12-sided polygonal space vector modulated 7-level Cascaded H-Bridge Multilevel Inverter are presented to validate the better performance of proposed strategy.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947439","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

DEARESt: Deep Convolutional Aberrant Behavior Detection in Real-world Scenarios 亲爱的:在现实世界场景中的深度卷积异常行为检测

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721378

K. Biradar, S. Dube, S. Vipparthi

引用次数: 5

Haar-like Local Ternary Pattern for Image Retrieval 图像检索的类haar局部三元模式

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721387

Megha Agarwal, A. Singhal

引用次数: 4

Approach to Non-Intrusive Load Monitoring using Factorial Hidden Markov Model 基于阶乘隐马尔可夫模型的非侵入式负荷监测方法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721436

Gautam A. Raiker, B. S. Reddy, L. Umanand, Aman Yadav, Mujeefa M. Shaikh

{"title":"Approach to Non-Intrusive Load Monitoring using Factorial Hidden Markov Model","authors":"Gautam A. Raiker, B. S. Reddy, L. Umanand, Aman Yadav, Mujeefa M. Shaikh","doi":"10.1109/ICIINFS.2018.8721436","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721436","url":null,"abstract":"What we measure, we can improve. In accordance to this approach, Indian Institute of Science (IISc), Bangalore has developed a Micro-grid Monitoring System in the campus through the installation of Smart Meters, covering almost 250 nodes including substations, centers, departments, administration, hostels and other utilities. This will help the institute in various ways such as capacity planning, substation loading, phase imbalance correction, over-voltage monitoring, billing and so on. Smart Meters measure the power consumption at a single point in the building giving a picture of the energy consumption of the building as a whole. It is necessary to also understand the scenario of the constituent loads at the point where the smart meter is installed so that ways could be found to reduce consumption. Personalised, concise and reliable feedback providing appliance level breakdown of energy consumption in the premises is the key in implementing energy efficiency programs. Taking this into consideration the area of Non Intrusive Load Monitoring (NILM) was explored. In NILM the aggregate smart meter data is separated into constituent loads by machine learning techniques. The NILM system is trained through previous data sets and then the algorithm will disaggregate the total power into individual appliances based on its experience. A benchmark NILM algorithm called Factorial Hidden Markov Model was used for proper load disaggregation. Finally an attempt was made to develop a Smartphone app to visualize results and bring the data to the people.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074780","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: 12