2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)最新文献_第10页

Analysis of Consumer Baseline for Demand Response Implementation: A Case Study 需求响应实施的消费者基线分析:一个案例研究

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9070878

Jayesh G. Priolkar, E. Sreeraj, Anita Thakur

{"title":"Analysis of Consumer Baseline for Demand Response Implementation: A Case Study","authors":"Jayesh G. Priolkar, E. Sreeraj, Anita Thakur","doi":"10.1109/SPIN48934.2020.9070878","DOIUrl":"https://doi.org/10.1109/SPIN48934.2020.9070878","url":null,"abstract":"Demand response (DR) refers to short term modification in electricity consumption pattern by consumers in terms of time and volume as per utility requirement. Implementation of DR helps utility for effective load management and consumers to avail of monetary and service benefits. The important component in evaluating the success of a DR implementation program is related to the accurate estimation of the consumer baseline load (CBL). A decision about load curtailment volume and incentives offering to the consumers is decided based on the estimation of CBL. A case study is performed for a domestic feeder of 33/11 kV substation of Goa state utility based upon the load and weather data. Different methods of CBL estimation computed based on the historical data and forecasting techniques are analyzed. In this paper, the Artificial Neural Network (ANN) based model is adopted for CBL estimation. From the computation of performance metrics, it is found that the proposed ANN method gives better performance in terms of higher accuracy, improved bias and variability over other estimation methods. The results obtained from ANN-based CBL estimation is used to analyze the impact of the implementation of price and incentive-based DR program on the consumer and state utility.","PeriodicalId":126759,"journal":{"name":"2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131110874","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

Analysis of Denoising Techniques applied to Facial Images for Emotion Recognition 面部图像去噪技术在情绪识别中的应用分析

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9070941

Sushil Kumar, Navin Prakash, Shivangi Agarwal

引用次数: 1

Emotion Classification Using Ensemble of Convolutional Neural Networks and Support Vector Machine 基于卷积神经网络和支持向量机集成的情感分类

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9071399

Anju Mishra, Archana Singh, P. Ranjan, A. Ujlayan

{"title":"Emotion Classification Using Ensemble of Convolutional Neural Networks and Support Vector Machine","authors":"Anju Mishra, Archana Singh, P. Ranjan, A. Ujlayan","doi":"10.1109/SPIN48934.2020.9071399","DOIUrl":"https://doi.org/10.1109/SPIN48934.2020.9071399","url":null,"abstract":"This paper presents an ensemble of convolutional neural networks (CNNs) and support vector machine (SVM) for classifying emotions from electroencephalogram (EEG) patterns. We used popular deep learning models for feature extraction and a support vector machine classifier is employed to classify the EEG patterns into suitable emotion classes. The main contribution of this work is to investigate on the following points: creating an ensemble of pre-trained deep learning networks with support vector machine classifier (SVM) for classifying emotional states of person for single and multiple emotional attributes. Finding out the best ensemble network, extracting suitable layer and robust features to improve the classification accuracy of support vector machine and finally to compare the performance of ensemble of networks with stand-alone deep learning networks. Two popular convolutional neural networks are used for experiments: Alex Net and GoogLeNet. All experiments are carried out on database for emotion analysis using physiological signals (DEAP). A thorough analysis of experimental results revealed that classification accuracy of 87.5% is achieved by ensemble of Alex Net and SVM for single attribute (valance) classification while for two attributes (arousal and valance) the accuracy achieved is 62.5%. Similarly, accuracy of 100% and 62.5% are achieved for single and two attributes classification respectively using ensemble of GoogLeNet and SVM.","PeriodicalId":126759,"journal":{"name":"2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"82 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132802235","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

CPU Performance Modeling through Analysis of Primitive Operations 基于原始操作分析的CPU性能建模

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9070898

V. K, M. Purnaprajna

{"title":"CPU Performance Modeling through Analysis of Primitive Operations","authors":"V. K, M. Purnaprajna","doi":"10.1109/SPIN48934.2020.9070898","DOIUrl":"https://doi.org/10.1109/SPIN48934.2020.9070898","url":null,"abstract":"Modern multi-core processors are complex because of their complicated memory hierarchies, superscalar issue of instructions, pipeline architecture, out-of-order execution and speculative execution due to branches in the program code. These features of the CPU are beneficial to improve the application performance. These processors have to be modelled to arrive at the trade-offs of design decisions such as power, time, throughput and latency. Modeling these complex micro-architectures is a very challenging task. In this work, we present a simple CPU modeling technique for data-parallel applications based on minimum offline profiling information and detailed static code analysis. This model, first identifies the primitive operations of the application kernels and then, based on the available offline profiled information, it estimates the performance of the given application kernel using either a SUM model or a MAX model. Experimental results show that an average estimation error of 7.19% and 41.4% is seen across data-parallel benchmarks from the Polybench suite for large and small problem sizes respectively on a multi-core CPU architecture.","PeriodicalId":126759,"journal":{"name":"2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"4 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133076336","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

SPIN 2020 Author Index SPIN 2020作者索引

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/spin48934.2020.9071327

引用次数: 0

A Miniaturized Antipodal Vivaldi Antenna for 5G Communication Applications 面向5G通信应用的小型化对映维瓦尔第天线

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9071075

Amruta S. Dixit, Sumit Kumar

{"title":"A Miniaturized Antipodal Vivaldi Antenna for 5G Communication Applications","authors":"Amruta S. Dixit, Sumit Kumar","doi":"10.1109/SPIN48934.2020.9071075","DOIUrl":"https://doi.org/10.1109/SPIN48934.2020.9071075","url":null,"abstract":"This paper reports the design of a miniaturized 1 X 4 Antipodal Vivaldi Antenna (AVA) for 5th generation (5G) communication applications. The size of proposed AVA is 28.8 mm X 24 mm x 0.254 mm and it is designed by using Rogers RT5880 substrate. Initially, 1 x 4 AVA array (AVA-A) is designed with slots in between two antenna elements. This designed AVA-A operates over three frequency bands which are 24.91 GHz to 33.18 GHz, 34.95 GHz to 36.58 GHz, and 38.34 GHz to 39.38 GHz and provides gain in the range of 8.47 dB to 12.63 dB. The performance parameters of AVA-A is enhanced by incorporating corrugations in it. The AVA-A with corrugations(AVA-AC) enhances gain, bandwidth and front to back ratio of an antenna. The proposed AVA-AC provides enhanced and nearly constant gain of 8.2 dB to 13.2 dB over 24.04 GHz to 40.85 GHz frequency range which includes three bands of 5G communication spectrum (24.25 GHz to 27.5 GHz, 31.8 GHz to 33.4 GHz, and 37 GHz to 40.5 GHz). Also, results shows that, AVA-AC provides stable radiation pattern over its complete operating range which makes AVA-AC suitable for integration in 5G communication devices.","PeriodicalId":126759,"journal":{"name":"2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114768365","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: 30

Wideband Ultra-thin Metamaterial Absorber for Ku & K- Band Applications 用于Ku和K波段的宽带超薄超材料吸收器

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9070938

Y. Khanna, Yogendra K Awasthi

引用次数: 5

Reduction of specific absorption rate (SAR) for human head using circular patch antenna 利用圆形贴片天线降低人体头部的比吸收率

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9071274

Alka Singla, A. Marwaha, S. Marwaha

引用次数: 5

Reversible Data Hiding Using Improved Gradient Based Prediction and Adaptive Histogram Bin Shifting 基于改进梯度预测和自适应直方图Bin移位的可逆数据隐藏

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9071246

Ravi Uyyala, R. Pal

引用次数: 7

Four Elements UWB MIMO Antenna Array 四元超宽频MIMO天线阵列

2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) Pub Date : 2020-02-01 DOI: 10.1109/SPIN48934.2020.9070922

Shailesh Jayant, Garima Srivastava

引用次数: 3