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DSVPWM-Based SSO-PIDR Control Scheme for Indirect Matrix Converter with Unity Power Factor Under Unbalanced Grid Voltage Condition

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Journal of The Institution of Engineers (India): Series B 2024年第5期105卷 1241-1258页

作者： Das, Sarat Kumar Moharana, Jayakrushna Syam, Prasid Department of Electrical Engineering Indian Institute of Engineering Science and Technology Shibpur711103 India Department of Electrical Engineering Hi-Tech Institute of Technology Odisha752057 India

Indirect matrix converter (IMC) is a direct ac-ac conversion system that not requires electrolytic or bulky capacitors. This system has a four-quadrant operation with input power factor control and also generates sinusoidal waveforms for output voltage with variable frequency and amplitude. For ideal operation, the rectification stage must be able to produce + ive dc-link voltage and sinusoidal input current with unity Grid Power Factor (GPF) even under an unbalanced voltage condition. Therefore, Shark Smell Optimization (SSO)-based Proportional Integral Derivative Resonant (PIDR) controller is proposed to operate the IMC switches to attain unity GPF and to maintain a balanced load voltage. The incorporation of derivative control with the existing Proportional Integral Resonant controller reduces the overshoot of the system and thus improves the transient response. Furthermore, the SSO algorithm helps to optimally tune the gain parameters of the PIDR controller. Finally, the 3-phase balanced output voltages with the preferred frequency and magnitude is produced using Double Space Vector Pulse Width Modulation technique. The results from analysis and experimental results illustrate that the system stability and the performance are improved. © The Institution of Engineers (India) 2024.

关键词： Controllers

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Whey protein peptide PEW attenuates hyperuricemia and associated renal inflammation in potassium oxonate and hypoxanthine-induced rat

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Food Bioscience 2023年第1期51卷 846-857页

作者： Xiaofen Qi Yanfeng Ma Kaifang Guan Chunhong Liu Rongchun Wang Ying Ma Tianjiao Niu School of Chemistry and Chemical Engineering Harbin Institute of TechnologyHarbin150001HeilongjiangChina Mengniu Hi-tech Dairy(Beijing)Co. LtdBeijing101107China

Pro-Glu-Trp(PEW),a whey protein-derived peptide,has been previously shown in vitro to have antihyperuricemic *** current study further evaluated the roles and the underlying mechanism of PEW in the management of hyperuricemia(HUA)in rat induced by potassium oxonate(PO)and *** revealed that PEW significantly reduced the levels of uric acid(UA),creatinine(Cr),and blood urea nitrogen(BUN)in serum,and effectively suppressed the activities of xanthine oxidase(XOD)associated with UA synthesis and modulated the expression of organic ion transporters related to UA ***,PEW alleviated UAinduced renal inflammation by regulating oxidative stress,suppressing the level of pro-inflammatory cytokines,and inhibiting the activation of NOD-like receptor family pyrin domain-containing 3(NLRP3)inflammasome and Toll-like receptor 4/myeloid differentiation factor 88/NF-kappaB(TLR4/MyD88/NF-κB)signaling *** together,these relults indicated that PEW improved HUA and renal inflammation by inhibiting UA synthesis,promoting renal UA excretion and suppressing NLRP3 inflammasome and TLR4/MyD88/NF-κB signaling pathways.

关键词： Peptide Hyperuricemia Organic ion transporters Oxidative stress NLRP3 inflammasome TLR4/MyD88/NF-κB signaling pathway

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A New Irrigation System Without Any External Sources

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Journal of The Institution of Engineers (India): Series A 2023年第2期104卷 281-289页

作者： Nayak, Ramesh Chandra Samal, Chandrika Roul, Manmatha K. Padhi, Payodhar Mechanical Engineering Department Synergy Institute of Technology Odisha Bhubaneswar India Mechanical Engineering Department GITA Autonomous College Odisha Bhubaneswar India Mechanical Engineering Department Hi-Tech Group of Institutions Odisha Bhubaneswar India

India is the second most populous country in the world and relies heavily on agriculture as its main source of income. The combination of agriculture and technology is needed to accelerate the development of agriculture, especially in rural areas, to increase productivity, efficiency and profitability. The application of technology in agriculture will enhance the farmer’s involvement in their occupation. Agriculture mainly consists of field grounding, transplantation, and harvesting. In each step of agriculture, irrigation is the main requirement. Farmers are spending more to irrigate their farms for agriculture. So, the application of technology to irrigation is the most important requirement to sustain the farmers. In this work, a pendulum-operated irrigation model is presented. The designed model is required to attach to the hand pump. The developed system will work without any external source of power, which will provide maximum profit to farmers. © 2023, The Institution of Engineers (India).

关键词： Profitability

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A review: Map-reduce (Hadoop) based data clustering for big data

A review: Map-reduce (Hadoop) based data clustering for big ...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Srivastava, Mili Kansal, hitesh Gautam, Aditi Shivani Ajay Kumar Garg Engineering Ghaziabad India Hi-tech Institute of Engineering College and Technology Ghaziabad India I.A.M.R Duhai Ghaziabad India SRM Institute of Science and Technology NCR Campus India

ISBN: (纸本)9781032669663

Clustering problems are becoming more challenging as the quantity of data and unstructured data handling complexities is increasing. Such big amount and complex data that cannot handle by two days DBMS tools is known as "Big Data". Processing time for data is directly y proportional to increase in data. To access (Read/write) big data efficiently data clustering is a good solution. Because of it differentiates between dissimilar data. Routine data clustering algorithms are considered as N P-Hard problem. To deal with such situation huge research is going on with respect to parallelization of resources and algorithms. Howes’er, parallelization gives the worst results when data is dependent on each other and dedicated setup is required. Alternative to Parallel architecture is a distributed environment and that can utilize remote system processing capacity y on the fly. Hadoop as a distributed environment and framework runs the same code on partitioned data, and finally gathers the result at one place. Hence, to solve the data clustering problems distributed environment is very helpful. Data classification or data distribution in distinct classes has an obligation to reduce the dependency. The availability of similar data at a single point from a big data is known as Data Clustering. To solve the data-clustering problem of big data distributing environment has proposed. This paper provides an in-depth info and future research idea about big data-clustering problems using distributed computing architecture. © 2024 The Author(s).

关键词： MapReduce

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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae112-bbae112页

作者： Zheng, Chuanyang Wang, Yixuan Cheng, Yuqi Wang, Xuesong Wei, Hongxin King, Irwin Li, Yu Department of Computer Science and Engineering CUHK Hong Kong SAR China College of Computing Georgia Institute of Technology Atlanta GA USA MLR Lab Southern University of Science and Technology The CUHK Shenzhen Research Institute Hi-Tech Park Nanshan Shenzhen 518057 China Institute for Medical Enginering and Science Massachusetts Institute of Technology Cambridge MA USA Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

关键词： novel rare cell discovery single-cell analysis neural network

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A Review on Solar Energy and Different Electricity Generations

A Review on Solar Energy and Different Electricity Generatio...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rani, Pooja Taya, Rashmi Reddy, V. Padmanabha G L Bajaj Institute of Technology and Management UP Greater Noida India Hi-Tech Institute of Engineering & Technology Electrical Engineering Department Ghaziabad India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350357769

Solar energy produced renewable energy with the help of sunlight and heat. Solar energy is the backbone of our world. Solar energy is eco-friendly renewable. But the non-renewable source of energy produced harmful and toxic gases in the environment and it has a negative effect on the environment and makes a non-friendly environment. Solar energy is very helpful in the production of electricity and produces less pollution. so, it is called green power. In this paper, we discussed different techniques used in the generation of electricity by converting solar energy into electrical energy like Photovoltaic cells, Solar Diesel Hybrid Systems, Solar Thermal Power plants, etc. The main purpose is to provide energy to everyone to fulfill of basic needs of the increasing population and for the growth of the industry. © 2023 IEEE.

关键词： Electricity Renewable energy solar energy

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Importance of the autoclave testing parameters on the initial stage of corrosion under CO2-containing geothermal environments

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Corrosion Communications 2024年第3期15卷 63-78页

作者： Yong Hua Zezhou Wen Hui Chen Yue Lv Xin Li Shaobin Wang Wei Huang Cheng Su Corrosion and Integrity Centre Zhejiang JIULI Hi-Tech Metals Co.Ltd Institute of Functional Surface School of Mechanical Engineering University of Leeds School of Materials and Environmental Engineering Shenzhen Polytechnic Qing Dao Sifang Kawasaki Rollingstock Technology Co. Ltd

Autoclave corrosion testing has been used for many years; however, few have studied the effect of its shortcomings on the formation of corrosion products and corrosion behaviour. In particular, understanding of these effects under high temperature and high pressure（HTHP） is rare. In this study, we highlighted the important parameters such as heating periods, flow rates and volume-to-surface ratios influencing carbon steel corrosion in the early stages. Especially, characterisation and protection behaviour of corrosion products associated with corrosion behaviour in the heating stage under the geothermal CO2-containing environment were systematically studied via mass loss, in-situ electrochemistry and surface analysis techniques. The roles of ■, volume-to-surface ratio and flow rate influence the formation of the corrosion products in the early stage are also evaluated.

关键词： Fe 3 O 4 Corrosion products Flow speeds Volume-to-surface ratios

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Integrated strategies on cobalt phosphides-based electrocatalysts for efficient hydrogen evolution reaction

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Tungsten 2024年第3期6卷 488-503页

作者： Thanh Dam Mai Ha Huu Do Faculty of Mechanical Engineering and Technology Ho Chi Minh City University of Industry and TradeHo Chi Minh City 700000Vietnam VKTech Research Center NTT Hi-Tech InstituteNguyen Tat Thanh UniversityHo Chi Minh City 700000Vietnam

The development of cost-effective and durable hydrogen evolution reaction(HER)electrocatalysts plays a vital role in dealing with the issues related to carbon dioxide *** phosphide-based nanomaterials are evaluated as promising advocates for HER due to high catalytic activities,good stability,and rich *** review commences with an exploration of the synthetic pathways of CoP,including solid-phase,solution-phase along with electrochemical ***,the mechanism of hydrogen formation is expressed thoroughly,after which various integrated strategies of morphology engineering with doping,assisted highly conductive materials,and construction of heterostructure were introduced for ***,burdensome tasks and possible guidance for the advancement of CoP-based nanomaterials were discussed for hydrogen production.

关键词： Cobalt phosphides Nanomaterials Electrocatalysts Hydrogen evolution reaction

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Real-time monitoring and fault detection in smart grids using IoT sensors and deep learning algorithm 4

Real-time monitoring and fault detection in smart grids usin...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Arnika Tyagi, Reshu Kumar, Pawan Sagar, Pramod Kumar Saraswat, Manish Bansal, Abhinav Manav Rachna University Department of Computer Science and Technology Haryana Faridabad India Abes Institute of Technology Department of Information Technology UP Ghaziabad India Hi-Tech Institute of Engineering and Technology Computer Science & Engineering UP Ghaziabad India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India

ISBN: (纸本)9798350364729

The concept of smart grids was released a long time ago, and since then the development in clever grids have changed our conventional energy machine with new advanced features like real-time monitoring power distribution control. This increased complexity brings with it the danger of errors and failures which can destabilize the system, leading to massive financial losses as well as social problems. In order to address this problem, we propose a real-time monitoring and fault detection system for smart grids via IoT sensors along with deep learning algorithms. This is able to encompass a network of Iot sensors which might be deployed in the course of the grid for real time facts on numerous parameters together with voltage, current and power glide. That number is then input into a deep getting to know set of rules, capable of detecting abnormal behavior and anomalies in the grid that can highlight faults or outages. In other words, the device is studying and learning about these facts all the time so it can be used as warning signs for grid operators who might still act proactively by avoiding any kind of matrix breakages. In addition the device can provide actual time color coded grid performance maps providing operators with the ability to track health of their own particular targeted area. Having green third-party maintenance will make your life and environment easier, decreasing downtime as well cost. Using an autonomous and durable strength supply, our suggested device is environmentally friendly along with a dependable answer for real-time tracking etc. fault detection in smart grids. © 2024 IEEE.

关键词： Smart power grids

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Deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images

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Research on Biomedical engineering 2023年第4期39卷 833-841页

作者： SenthilPrakash, T. Kannan, G. Prabhakaran, Salini Prajapati, Bhagirath Parshuram Department of Computer Science and Engineering Shree Venkateshwara Hi-Tech Engineering College Tamil Nadu Gobichettipalayam India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Computer Engineering A. D. Patel Institute of Technology Gujarat New Vallabh Vidyanagar India

Purpose: Breast cancer (BC) is a leading cause of death among women. Several existing methods on breast cancer classification were suggested with machine learning, but none of them accurately classify the breast cancer and requires high computational time. To overcome these problems, deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images (DCSNN-BCDC-MI) is proposed in this manuscript. Methods: Initially, input breast mammography pictures are taken from Digital Database for Screening Mammography (DDSM) data set. The input breast mammography images are pre-processed and feature extracted. The DCSNN is employed to categorize pictures using retrieved characteristics, such as benign and malignant. Results: The proposed DCSNN-BCDC-MI technique is implemented at PYTHON. The performance of the proposed DCSNN-BCDC-MI technique attains 31.13%, 12.78%, 14.38%, 19.33%, and 19.11% high accuracy and 61.08%, 66.42%, 66.54%, 50.26%, and 53.03% low computation time comparing to the existing techniques, like deep learning fed capsule neural network for breast cancer detection utilizing mammogram images (OMLTS-DLCN-BCDC-MI), extended model for tumor location detection based on deep learning (VGG-CNN-BCDC-MI), deep learning algorithm for automated identification with breast cancer classification depending on transfer-learning strategy (VGG16-BCDC-MI), deep learning method for breast cancer identification on mammography by learning certain cancerous mass representations (CNN-Deep AE-BCDC-MI), and automatic identification with mammograms classification dependent on increased extreme learning machine along deep learning (ICS-ELM-BCDC-MI), respectively. Conclusion: The simulation result exemplifies that the proposed method can be capable to determine the optimum global solutions efficiently with the exact existing methods. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Enginee

关键词： Mammography

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