2020 International Conference on Communication, Computing and Industry 4.0 (C2I4)最新文献

Use of Image Processing Techniques to Investigate the Behaviour of Driven Piles 利用图像处理技术研究打桩桩的行为

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368912

G. Sreelakshmi, M. Asha

{"title":"Use of Image Processing Techniques to Investigate the Behaviour of Driven Piles","authors":"G. Sreelakshmi, M. Asha","doi":"10.1109/C2I451079.2020.9368912","DOIUrl":"https://doi.org/10.1109/C2I451079.2020.9368912","url":null,"abstract":"Steel driven piles having either open-ended and closed ends have been used widely in onshore and offshore pile foundations. But the behaviour of open-ended piles is complicated than closed-end piles due to the formation of soil clogging phenomenon. The current work examines the effect of pile dimensions and its related clogging behaviour through image analysis. Solid and hollow half-section aluminium piles of different elastic modulus and dissimilar cross-sectional areas are used for the study. A scaling factor of 10 has been used for modelling the pile. The modelled piles are subjected to Standard Penetration Test (SPT) which simulates the application of impact loads in crushed stones (4.75-2.36 mm) of various soil densities. The experimental progression is grabbed through the digital single-lens reflex camera and sequential pictures captured during the test are analyzed through open-source software GeoPIV which is based on Digital Image Correlation technique (DIC). The GeoPIV software helps in measuring deformation behaviour and strain path over a Region of Interest (ROI). The examination of DIC test results revealed that for hollow piles there is a surge in volumetric shear strain contours about pile shaft region. This could be due to the soil plug formation at the tip of hollow pile irrespective of infill density. For solid piles, strain concentrations are observed closer to pile head due to its greater flexural rigidity than hollow piles.","PeriodicalId":354259,"journal":{"name":"2020 International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122591105","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

Pilot Assisted MIMO OFDM-IM Design for Doubly Selective Underwater Acoustic Systems 双选择性水声系统的导频辅助MIMO OFDM-IM设计

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368940

Mhd Tahssin Altabbaa

引用次数: 2

Neighbourhood Based Bi-Level Contrast Adjustment for Underwater Image Enhancement Using Modified Particle Swarm Optimization 基于邻域双水平对比度调整的改进粒子群优化水下图像增强

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368902

S. Paul, S. De, Sandip Dey

引用次数: 0

Discrete Sliding Mode Controlled Cuk Converter for PV fed Highly Transient Loads 用于PV供电高暂态负载的离散滑模控制Cuk变换器

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368959

V. K, K. Chitra, B. D

引用次数: 2

Sparse Channel Estimation Algorithm for Doubly Selective MIMO OFDM-Based UWAC Systems With Double Focusing 基于双聚焦双选择MIMO ofdm的UWAC系统稀疏信道估计算法

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368905

Mhd Tahssin Altabbaa

引用次数: 0

Reservoir Inflow Prediction using Multi-model Ensemble System 基于多模型集合系统的水库入流预测

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368942

K. S, G. V, Prasad B S

引用次数: 4

CNN based Categorization of respiratory sounds using spectral descriptors 基于CNN的呼吸声音分类，使用频谱描述符

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368933

S. Jayalakshmy, B. Priya, N. Kavya

{"title":"CNN based Categorization of respiratory sounds using spectral descriptors","authors":"S. Jayalakshmy, B. Priya, N. Kavya","doi":"10.1109/C2I451079.2020.9368933","DOIUrl":"https://doi.org/10.1109/C2I451079.2020.9368933","url":null,"abstract":"Chronic Obstructive Pulmonary Disease (COPD) is a dreadful disease which is a wide umbrella comprises of emphysema, bronchitis etc. It threatens the life of almost nearly 3 million people all over the world. The diagnosis of COPD can be detected in a better manner based on the lung sound analysis with the help of deep learning models such as convolutional neural network (CNN). In this work, the presence of COPD with different class of the sound like normal breathe sounds and abnormal breathe sounds such as wheeze, crackle and rhonchi are classified by using multi-class classifier. Spectral descriptor features from linear spectrum and MFCC from Mel spectrum are extracted. For experimentation and classification, a total of 596 lung sound signals are considered in this work. The classifier such as K-NN and decision tree are used to obtain an improved accuracy compared to binary machine learning classifier. The results indicates than an overall accuracy of 96.7% is obtained with multi-class classifiers using deep learning CNN model. The multi-class classifier results are also compared with SVM classifier.","PeriodicalId":354259,"journal":{"name":"2020 International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121706130","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

Performance Analysis of Robust Reference Current Tracking Controllers for Single Phase LCL-type Grid Connected Converter through Active Damping Approach 基于主动阻尼法的单相lcl型并网变换器鲁棒参考电流跟踪控制器性能分析

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368916

Shaletta Elias, Supritha S, Hemachandra Gudimindla

{"title":"Performance Analysis of Robust Reference Current Tracking Controllers for Single Phase LCL-type Grid Connected Converter through Active Damping Approach","authors":"Shaletta Elias, Supritha S, Hemachandra Gudimindla","doi":"10.1109/C2I451079.2020.9368916","DOIUrl":"https://doi.org/10.1109/C2I451079.2020.9368916","url":null,"abstract":"The LCL filter plays a major role to attenuate the switching frequency harmonics due to the power electronic converter. Furthermore, LCL Filters are naturally resonant which affect the stability of the system. To tackle the resonance problem, generally passive damping methods are a simple and straight forward solution but it effects the efficiency due to the insertion of resistance in LCL components. It is important to explore the alternatives for the passive damping methods to improve the overall system efficiency. This paper presents, the design of reference current tracking controller based on the LCL filter Capacitor feedback current using active damping approach. Active damping technique used in the proposed controller helps to suppress the resonant peak and injects high quality power into the grid. Further, the impact of LCL filter parameters on robustness of the proposed controllers is studied. Finally, performance of the proposed resonant controller is compared with the existing PI controller through MATLAB simulations.","PeriodicalId":354259,"journal":{"name":"2020 International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115536126","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

Low-Cost FPGA Based Emulator for Spacecraft Power Systems Hardware in Loop testing 基于低成本FPGA的航天器电源系统硬件在环测试仿真器

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368932

S. V, P. K. Peter

引用次数: 1

Breast Cancer Prediction Analysis using Machine Learning Algorithms 使用机器学习算法进行乳腺癌预测分析

2020 International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2020-12-17 DOI: 10.1109/C2I451079.2020.9368911

Vinayak A. Telsang, K. Hegde

{"title":"Breast Cancer Prediction Analysis using Machine Learning Algorithms","authors":"Vinayak A. Telsang, K. Hegde","doi":"10.1109/C2I451079.2020.9368911","DOIUrl":"https://doi.org/10.1109/C2I451079.2020.9368911","url":null,"abstract":"Most common diseases and the leading cause of death to most women across the globe is Breast Cancer (BC). Although many individuals who suffer breast cancer have no family history but women who have blood relatives suffering from the same disease are at higher risk. Besides, a high risk of developing breast cancer includes aging, genes, thick breast tissues, obesity, and radiation exposure. Malignant and benign are two different types of tumors and to distinguish between these two, physicians need a reliable diagnostic procedure. The mammography method is used to detect breast cancer but radiologists exhibit significant variation in interpretation. Fine Needle Aspiration Cytology (FNAC) is commonly adopted in the diagnosis of breast cancer. Moreover, early diagnosis is vital to treatment with a better chance of success. Classification and data mining attributes are an efficient and effective way of categorizing results. Using machine learning models that will play a vital role in early prediction. In this paper, we present a prediction of breast cancer with different machine learning algorithms compare their prediction accuracy, area under the receiver operating characteristic curve (AUC) and performance parameters. For Simulation purposes, we are using the Wisconsin Dataset of Breast Cancer (WDBC). After analysis, the Support Vector Machine (SVM) model has achieved 96.25% accuracy with AUC of 99.4. Further, these algorithms can be modified with their mathematical models to increase the prediction of breast cancer.","PeriodicalId":354259,"journal":{"name":"2020 International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122435899","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