2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)最新文献_第5页

Identification of underground cable fault location and development 地下电缆故障定位与发展的识别

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073476

M. Hans, Snehal C. Kor, A. S. Patil

{"title":"Identification of underground cable fault location and development","authors":"M. Hans, Snehal C. Kor, A. S. Patil","doi":"10.1109/ICDMAI.2017.8073476","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073476","url":null,"abstract":"As India emerging as a developing country, civilized area is also increasing day by day. As underground cables are best under such conditions its utilization is also growing because of its obvious advantages like lower transmission losses, lower maintenance cost and they are less susceptible to the impacts of severe weather and so many. But it is having few disadvantages too like expensive installation and detection of fault location. As it is not visible it becomes difficult to find exact location of the fault. In this paper we present two methods which will be very useful to identify the exact distance of fault of underground system from base station. One of the methods is Murray loop method and other one is Ohm's Law Method. Murray loop method uses the whetstone bridge to calculate exact distance of fault location from base station and sends it to the user mobile. Whereas in Ohm's law method, when any fault occurs, voltage drop will vary depending on the length of fault in cable, since the current varies. Both the methods use voltage convertor, microcontroller and potentiometer to find the fault location under LG, LL, LLL faults.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116688135","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: 16

The impact of corporate governance and firm performance on chief executive officer's compensation: Evidence from central state owned enterprises in India 公司治理和公司绩效对首席执行官薪酬的影响:来自印度中央国有企业的证据

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073491

Sangeetha Gunasekhar, K. Dinesh

引用次数: 3

Intermittent demand forecasting for long tail SKUs 长尾库存单位的间歇性需求预测

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073537

Arnab Ghosh Dastidar

引用次数: 0

Voltage-lift DC-DC converters for photovoltaic application-a review 光伏应用升压型DC-DC变换器综述

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073505

V. Savakhande, C. L. Bhattar, P. L. Bhattar

引用次数: 11

Block-Based Quantized Histogram (BBQH) for efficient background modeling and foreground extraction in video 基于块的量化直方图(BBQH)用于视频中高效的背景建模和前景提取

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073514

Satyabrata Maity, A. Chakrabarti, D. Bhattacharjee

引用次数: 5

Video compression using DWT algorithm implementing on FPGA 视频压缩采用DWT算法在FPGA上实现

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073481

G. Joshi, Nilesh P. Bhosale

引用次数: 3

A 180 nm efficient low power and optimized area ALU design using gate diffusion input technique 基于栅极扩散输入技术的180nm高效低功耗优化面积ALU设计

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073484

M. Mukhedkar, Wagh Bhavesh Pandurang

引用次数: 6

Forecasting of sales by using fusion of machine learning techniques 利用融合机器学习技术预测销售

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073492

M. Gumani, Yogesh Korke, P. Shah, Sandeep S. Udmale, Vijay Sambhe, S. Bhirud

{"title":"Forecasting of sales by using fusion of machine learning techniques","authors":"M. Gumani, Yogesh Korke, P. Shah, Sandeep S. Udmale, Vijay Sambhe, S. Bhirud","doi":"10.1109/ICDMAI.2017.8073492","DOIUrl":"https://doi.org/10.1109/ICDMAI.2017.8073492","url":null,"abstract":"Forecasting is an integral part of any organization for their decision-making process so that they can predict their targets and modify their strategy in order to improve their sales or productivity in the coming future. This paper evaluates and compares various machine learning models, namely, ARIMA, Auto Regressive Neural Network(ARNN), XGBoost, SVM, Hy-brid Models like Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM and STL Decomposition (using ARIMA, Snaive, XGBoost) to forecast sales of a drug store company called Rossmann. Training data set contains past sales and supplemental information about drug stores. Accuracy of these models is measured by metrics such as MAE and RMSE. Initially, linear model such as ARIMA has been applied to forecast sales. ARIMA was not able to capture nonlinear patterns precisely, hence nonlinear models such as Neural Network, XGBoost and SVM were used. Nonlinear models performed better than ARIMA and gave low RMSE. Then, to further optimize the performance, composite models were designed using hybrid technique and decomposition technique. Hybrid ARIMA-ARNN, Hybrid ARIMA-XGBoost, Hybrid ARIMA-SVM were used and all of them performed better than their respective individual models. Then, the composite model was designed using STL Decomposition where the decomposed components namely seasonal, trend and remainder components were forecasted by Snaive, ARIMA and XGBoost. STL gave better results than individual and hybrid models. This paper evaluates and analyzes why composite models give better results than an individual model and state that decomposition technique is better than the hybrid technique for this application.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134606597","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: 29

PRIVACY preservation in big data using anonymization techniques 使用匿名化技术的大数据隐私保护

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073538

Tanashri Karle, D. Vora

引用次数: 20

Development of fault current limiters: A review 故障限流器的发展综述

2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) Pub Date : 2017-02-01 DOI: 10.1109/ICDMAI.2017.8073496

Sagar S. Patil, A. Thorat

引用次数: 7