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Early diagnosis and monitoring of sleep apnea using desaturation index and respiration percentage

AIP Conference Proceedings 2022年第1期2405卷

作者： P. Sinthia M. Malathi B. Sathya Sri K. Sankar M. Nalini 1Department of Biomedical Engineering Saveetha Engineering College Chennai 602105 India 2Department of Biomedical Engineering Rajalakshmi Institute of Technology Chennai 600 124 India 3Department of Electronics and Communication Engineering Vel Tech Rangarajan DrSagunthala R&D Institute of Science and Technology Chennai 600 062 India 4Department of Computer Science Engineering Saveetha School of Engineering Chennai 602105 India

Obstructive sleep apnea (OSA) is one of the underdiagnosed sleep disorders in which the respiratory airpaths are disturbed and weakens the respiration process. Apnea refers to the termination of flow of air to the lungs which persists at least for 10 seconds. It is a serious factor for several conditions like hypoxia, hypertension, cardiovascular diseases including stroke. Presently, Polysomnography (PSG) taken over full night duration is implemented as standard method for OSA assessment. Over hundred million people around the world suffer from sleep apnea. This includes, 3- 7% men and 2-5% women. Yet there is a lack of patient friendly medical devices for long term monitoring of OSA. This is because the current systems are rather bulky, expensive, intrusive and unavailable in a comfortable environment for patients. The proposed system aims at developing a wearable, convenient, inexpensive and compact device for long term monitoring of OSA. Our approach is based on monitoring the patient using a double channel system of respiration rate and saturation pressure of oxygen. The data regarding the occurrence of events is collected and generated as an overnight report which can be sent to the patient/caregiver/doctor. In emergency cases an alarm will be triggered and the patient will be woken up from sleep.

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Corrigendum to “Revealing an OSELM based on traversal tree for higher energy adaptive control using an efficient solar box cooker” [Sol. Energy 218 (2021) 320–336]

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Solar Energy 2022年 237卷 484-485页

作者： P. Thamizharasu S. Shanmugan S. Sivakumar Catalin I. Pruncu A.E. Kabeel J. Nagaraj Lakshmi Sarvani Videla K. Vijai Anand L. Lamberti Meena Laad Department of Physics Koneru Lakshmaiah Education Foundation Green Fields Guntur District Vaddeswaram Andhra Pradesh 522502 India Muthayammal College of Arts & Science (Affiliated to Periyar University – A Unit of VANETRA Group) Vengayapalayam Kakkaveri Rasipuram Namakkal Tamil Nadu 637408 India Research Centre for Solar Energy Department of Physics Koneru Lakshmaiah Education Foundation Green Fields Guntur District Vaddeswaram Andhra Pradesh 522502 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Guntur District Vaddeswaram Andhra Pradesh 522502 India Department of Design Manufacturing & Engineering Management University of Strathclyde Glasgow G1 1XJ UK Department of Mechanical Engineering Imperial College London Exhibition Rd. London SW7 2AZ UK Mechanical Power Engineering Department Faculty of Engineering Tanta University Egypt 31527 Egypt Faculty of Engineering Delta University for Science and Technology Gamasa Egypt Department of Physics Sathyabama Institute of Science and Technology Chennai 600 119 India Dipartimento di Meccanica Matematica e Management Politecnico di Bari 70125 Bari Italy Department of Applied Science Symbiosis Institute of Technology Symbiosis International Deemed University Near Lupin Research Park Gram: Lavale Tal: Mulshi Pune Maharashtra 412115 India

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FMNet: A novel hybrid face mask detection using deep learning

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AIP Conference Proceedings 2022年第1期2463卷

作者： D. Ravikumar T. Jaya S. Harish Kumar R. Vishal R. Rokesh S. Hariharan 1Department of Electronics and Communication Engineering Kings Engineering College Chennai India 2Departments of Electronics & Communication Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India

Face Mask recognition has developed as an extremely admired issues in several domains such as image processing, computer vision and artificial intelligence. Several novel approaches are being developed through deep learning based Convolutional Neural Network model to detect the face mask identification. In this paper, we propose “FMNet” model which indicates Face Mask detection Neural Network model for recognition of face mask person on publicly available face masks images as resources. Furthermore, we applied image preprocessing, feature extraction have performed using CNN, applied FMNet model for face mask recognition from images, and finally perform classification technique to categorize the images as person “wearing masks” and “not wearing masks”. Our experimental outcome generates better performance in finding face mask by achieving accuracy as 99.7%. Moreover, comparing our proposed algorithm results with pre-trained models namely VGG 16 attains 99.6%. This system has capability to perform in real time application creates it applicable to identify people in airports, buses, schools etc.

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An observational assessment of the Indian construction industry use of total quality management

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AIP Conference Proceedings 2023年第1期2690卷

作者： P. M. Rameshwaran Santosha Kumar Mallick Vipin Mahadeven Prabu Baskar Anoop Narain Singh S. Arvindan 1Department of Civil Engineering SRM Institute of Science and Technology Ramapuram Chennai India 2Department of Business Administration Utkal University Bhubaneswar India 3Department of Civil Engineering Rajkiya Engineering College Azamgarh Uttar Pradesh India 4Department of Civil Engineering Sona College of Technology Salem Tamilnadu India 5Department of Civil Engineering Anand School of Architecture Kazhipattur Tamilnadu India

Construction has long been a driver of economic growth, and India, as the world's second-fastest-growing economy, has seen explosive growth in this sector, particularly in the last decade. The construction industry accounts for more than 11% of India's GDP, and its expansion is essential to the country's overall development. Because of its importance, a lot of research has gone into TQM implementation, which has a direct impact on the industry's success. The issues and perspectives facing the Indian construction sector are investigated in this paper using a rigorous methodology. Based on eight CSFs that are recognised as the most important contributors to the sector's operational and organisational performance, this study developed a model for TQM adoption in the construction industry in general, and the Indian construction industry in particular.

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CEPC Technical Design Report

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Radiation Detection technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

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Unattended detection in neural OCT image sequences of disease markers

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AIP Conference Proceedings 2022年第1期2519卷

作者： V. Anbarasu Sharanabasappa C. Gandage Z. Vakil V. Antony Asir Daniel 1Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India 2Department of Computer Science and Engineering Poojya Doddappa Appa College of Engineering Hyderabad Karnataka India 3Department of Electronics and Communication Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India 4Francis Xavier Engineering College Tirunelveli Tamil Nadu India

In treatment, initial diagnosis and the treatment of illness, detection, and quantitative analysis of markers in medical images are important. Supervised computer education allows results to be identified and exploited a priori after the samples of instruction by experts are annotated. But due to the number of required examples of instruction and the restriction of the marker language to recognized individuals, monitoring does not scale well. In this proof of concept research, we suggest unattended recognition of anomalies as marker applicants in retinal optical coherent imagery without a prior description restriction. We classify and categorize marker applicants that often appear in the data and prove that these markers provide a predictive value for the disease detection role. A cautious qualitative study of the established data control indicators shows, in patients with early and late-age macular degeneration, how their quantifiable frequency fits our present knowledge of the diseases. A deep denoise auto encoder is trained in multi-scaled images, and any anomalies are detected in new data with the one-class support vector machine. The anomaly cluster distinguishes stable groups. With these markers, the precision of 81,40% is measured as safe, early AMD, and late AMD cases. The model obtained a region under the Receiver operator of hackney in a second binary classification experiment with publicly accessible safe and mid-Dexter datasets.

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Green synthesis of europium doped titanium dioxide nanoparticle and its photocatalytic application

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AIP Conference Proceedings 2022年第1期2464卷

作者： S. Mohamed Wahid T. Pushpa Malini G. Puthilibai S. Chitradevi 1Department of Chemistry SRM Institute of Science and Technology Kattankulathur 603 203 Tamil Nadu India 2)Department of Chemistry Sri Sairam Engineering College Chennai 600 004 Tamil Nadu India 3)Department of Chemistry Rajalakshmi Institute of Technology Chennai 602 104 Tamil Nadu India

The Scientific interest of rare earth elements is constantly increasing owing to its multifold applications. Europium is a significant rare earth element which has a strong local magnetic moment and also exhibit phosphorescence. In this study the significance of Europium was further explored in photocatalytic aspect by doping it on Titanium dioxide nanoparticle. Eu doped TiO2 was synthesized using Psidium guajava leaf extract as a reducing agent. The synthesized nano particle was characterized using SEM, TEM, XRD, Edax and FTIR techniques. The characterization data confirmed the formation of the Eu doped nanoparticle. The photocatalytic efficiency of Eu was determined by comparing the degradation rate of methylene blue dye using nano TiO2 catalyst with and without Europium as dopant. Eu doped TiO2 showed greater photocatalytic efficiency when compared to the undoped nanomaterial.

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Smart garbage segregation for the smart city management systems

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AIP Conference Proceedings 2021年第1期2317卷

作者： T. S. Arulananth M. Baskar R. Divya Sree P. Sai Teja T. Santhosh Kumar 1Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad -43 Telangana India 2Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai. Tamilnadu India-603203

Rapid raise in urbanization and population has led to the accumulation of huge amount of garbage throughout the world which became a serious problem that requires proper management and recycling. Proper recycling of garbage can be done easily if it is segregated prior to the recycling process. This project aims to present a smart garbage segregation system which segregates garbage into its respective type (wet waste, dry waste, metallic waste etc.) using different types of sensors and Arduino controller which takes care of all the task. The entire task is performed on a conveyor belt having multiple sections depending on number of types of wastes one wants to segregate. Waste is detected and directed to the respective slot or section for segregation. Then various piles of waste are created which are moved to their respective recycling units without much human effort.

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Internet security protocol for secure data transmission using OSPF and BGP

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AIP Conference Proceedings 2022年第1期2463卷

作者： D. Ravikumar M. Prasath Nikhil U. Kumar V. Devi P. Vijayalakshmi 1Department of Electronics and Communication Engineering Kings Engineering College Chennai India 2Departments of Electronics & Communication Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India 3Departments of Computer Applications Gurunanak College Chennai India

Internet Protocol Security (IPsec) is a secure network protocol suite that authenticates and encrypts data packets to offer safe encrypted communication between two computers over an IP network. It’s a protocol that’s utilized in virtual private networks. In this proposed work, we are providing secure path for better packet transmission between transmitter and receiver. For safe and secure data transmission, we are developing two routing algorithms namely Open Shortest Path First and Border Gateway Protocol which are designed for packaging along with switch routing. This embedded algorithm of both OSPF and BGP protocols enhances Internet Protocol security for secure data transmission in networking domain.

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Deep learning enabled smart charging technology for electric vehicles

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AIP Conference Proceedings 2022年第1期2527卷

作者： T. Blesslin Sheeba C. Sharanya C. Nayanatara S. K. Indumathi K. Kalins G. Ignisha Rajathi 1Department of ECE R.M.K. Engineering College Chennai India 2Department of ECE Vels Institute of Science Technology and Advanced Studies Chennai India 3Department of EEE Sri Sairam Engineering College Chennai India 4Centre for Automation and Robotics Hindustan Institute of Technology and Science Chennai India 5Department of Science and Humanities Sri Krishna College of Engineering and Technology Coimbatore India 6Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar.

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