2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)最新文献_第2页

Comparative performance of different wavelets in Power Quality disturbance detection and quantification 不同小波在电能质量扰动检测与量化中的性能比较

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802875

S. Divya, K. Uma Rao

引用次数: 2

Photovoltaic based PMDC motor drive system using MPPT 基于MPPT的光伏PMDC电机驱动系统

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802866

Chetanakumar Hadimani S, A. Raju, R. Radha, Rohini Jyoti

{"title":"Photovoltaic based PMDC motor drive system using MPPT","authors":"Chetanakumar Hadimani S, A. Raju, R. Radha, Rohini Jyoti","doi":"10.1109/CCIP.2016.7802866","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802866","url":null,"abstract":"In power generation, renewable energy plays an important role. With the increase in the energy demand, there is a need for a renewable energy source that will fulfil the required demand and also which is environmental friendly. When sunlight falls on a Photovoltaic (PV) module, direct current (dc) is produced and little maintenance is required for PV modules. Maximum Power Point Tracker (MPPT) helps in ensuring that the maximum power is transferred to the load. Design, simulation and implementation of MPPT for PV module using PIC digital signal processor are mentioned in the presented work. A buck/stepdown dc-dc converter in conjunction with proper control is used to perform MPPT. Numerous methods of MPPT algorithm are available, one among them is “Perturb and Observe method (P&O)”, which operates by periodically incrementing or decrementing PV module terminal voltage or current and comparing. In P&O method present PV output power is compared with the previous set of perturbation cycle. For achieving maximum power point tracking, the P&O control algorithm for the adjustment of the step size of the duty ratio of the buck/stepdown dc-dc converter is made. Here Buck converter is used as starter as well as to meet voltage level required for driving Permanent Magnet Direct Current (PMDC) motor. Since both power and voltage variations are considered in this algorithm, it allows better performance of MPPT in driving PMDC motor.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130522311","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

An ensemble approach to detect exudates in digital fundus images 数字眼底图像中渗出物的集成检测方法

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802870

B. Shilpa, T. N. Nagabhushan

{"title":"An ensemble approach to detect exudates in digital fundus images","authors":"B. Shilpa, T. N. Nagabhushan","doi":"10.1109/CCIP.2016.7802870","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802870","url":null,"abstract":"Fundus Image analysis is a major concern with respect to various disease detection. Diabetic retinopathy (DR) is seen in patients suffering from diabetes mellitus type 2 which leads to blindness. Fundus images are used to identify abnormalities like microaneurysms, haemorrhages, cotton wool spots, exudates, venous beading, and optic disc oedema that cause DR. Automated diagnosis of DR gives first-hand information about the disease presence, and save diabetic patients from vision loss. This paper presents a novel ensemble approach to automatically detect exudates in the fundus images. Normal background features are removed initially. Morphological operations combined with logical operations is the ensemble approach that has enhanced the detection and marking of exudates. Publicly available standard database DIARETDB1 and images of Forus Health is used to experiment the algorithm. 89.6% of specificity, 100% of sensitivity is obtained and evaluated with logistic regression classifier. Also, 89.13% of positive predictive value and 100% negative predictive value is obtained with this approach. The AUC of ROC plot obtained is 0.969.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126324001","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: 8

Optimized Radial Basis Function Neural Network model for wind power prediction 风电功率预测的优化径向基函数神经网络模型

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802846

Rashmi P. Shetty, A. Sathyabhama, S. Pai .P, A. Adarsh Rai

引用次数: 19

Interaction of auditory perceptual dimensions: A review 听觉知觉维度的相互作用:综述

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802882

S. Arthi, T. V. Sreenivasi

引用次数: 0

Meta-cognitive extreme learning machine for regression problems 回归问题的元认知极限学习机

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802886

K. N. Krishna, R. Savitha, A. Al Mamun

{"title":"Meta-cognitive extreme learning machine for regression problems","authors":"K. N. Krishna, R. Savitha, A. Al Mamun","doi":"10.1109/CCIP.2016.7802886","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802886","url":null,"abstract":"In this paper, we present an efficient fast learning algorithm for regression problems using meta-cognitive extreme learning machine(McELM). The proposed algorithm has two components, namely the cognitive component and meta-cognitive component. The cognitive component is an extreme learning machine (ELM) while the meta-cognitive component which controls the cognitive component employs a self-regulating learning mechanism to decide what to learn, when to learn and how to learn. The meta-cognitive component chooses suitable learning method based on the samples presented namely, delete sample, reserve sample and network update. The use of ELM improves the network speed and reduces computational cost. Unlike traditional ELM, the number of hidden layers is not fixed priori in McELM, instead, the network is built during the learning phase. This algorithm is evaluated on a set of benchmark regression and approximation problems and also on a real-world wind force and moment coefficient prediction problem. Performance results in this study highlight that McELM can achieve better results compared with conventional ELM, support vector regression (SVR).","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116364946","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: 2

Speech therapy system to Kannada language 语言治疗系统，以卡纳达语

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802889

V. Udayashankara, Swapna Havalgi

引用次数: 3

On improving energy saving in ieee 802.11s wireless mesh networks under mobility conditions 移动条件下ieee 802.11s无线网状网络节能改进研究

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802871

S. P. Shiva Prakash, T. N. Nagabhushan, K. Krinkin

{"title":"On improving energy saving in ieee 802.11s wireless mesh networks under mobility conditions","authors":"S. P. Shiva Prakash, T. N. Nagabhushan, K. Krinkin","doi":"10.1109/CCIP.2016.7802871","DOIUrl":"https://doi.org/10.1109/CCIP.2016.7802871","url":null,"abstract":"Owing to the self organizing, self configuring and self healing capabilities, Wireless Mesh Networks (WMN) have emerged as most powerful architectures in recent times. WMN operate with limited battery resources. To save energy, ieee 802.11s has introduced a mechanism called power save mode(PSM) which switches STA mode from active to light sleep or deep sleep mode when STA is not involved in transmission. Several models have been proposed by researchers to improve the energy saving mechanisms in 802.11s. Most of the researchers have considered node position as static while proposing their model. In WMN nodes are subjected to change their position over a period of time depending on the mobility mode and pattern. Under mobility conditions there could be higher energy consumption at STA compared to static cases. Hence in this work, we propose an energy saving model that involves STA mobility and trigger power save modes based on the remaining energy at each STA. The experiments have been conducted using RandomWayPoint mobility model in Network Simulator3. The behaviour of the model is observed by keeping STA position dynamic. The results show that energy consumption rate of STA is high under mobility conditions compared to static. By Implementing PSM under mobility situations, we are able save substantial energy thus increasing the life of the batteries associated with each STA in a WMN.","PeriodicalId":354589,"journal":{"name":"2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122700077","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: 4

Remote control of appliances based on Raspberry pi 基于树莓派的电器远程控制

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802863

M. Rukmini, D. G. Gayathri Devi

引用次数: 5

Video frame segmentation by using newton's law of gravity 基于牛顿引力定律的视频帧分割

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI: 10.1109/CCIP.2016.7802847

A. Ghasemi, C. R. Ravi Kumar

引用次数: 1