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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Vadthya Jagan Kutikanti Swetha Kalagotla Chenchireddy Kottha Aruna Neelima Kalahasthi Shabbier Ahmed Sydu Department of Engineering and Electronics Engineering Vignana Bharathi Institute of Technology Hyderabad India Department of Engineering and Electronics Engineering Geethanjali College of Engineering and Technology Hyderabad India College of Engineering and Technology University of Technology and Applied Sciences Al-Musannah Oman

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

A modular five-level H-bridge current source inverter with a switched inductor and capacitor arrangement is presented in this study. This current source inverter arrangement operates in a closed loop, while the traditional current source inverter has an open circuit issue. The conventional current source inverter has an open circuit problem, whereas this current source inverter configuration functions in a closed loop. As a result, providing overlap compensation improves its dependability while decreasing the overlap time. Overlap compensation improves the quality of the output waveform, while the overlap duration will damage the ac output current waveform. An analysis of the current source inverter's operation is offered in this research. The given current source inverter has less overall harmonic distortion than the traditional current source inverter., and the switched inductors are used to maintain a constant input current, while the switched capacitors are provided for n-level analysis to mitigate the open circuit problem. POWER-SIM software is used to verify the simulation findings of this research.

关键词： Capacitors Switches Pulse width modulation Inverters Software Reliability Inductors Transient analysis Protection Switching circuits

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Forecasting Construction Cost of Pipelaying Projects Using Backpropagation Artificial Neural Network and Multiple Linear Regression 4th

Forecasting Construction Cost of Pipelaying Projects Using B...

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4th International Civil engineering and Architecture Conference, CEAC 2024

作者： Melog, Norrodin V. Silva, Dante L. Diona, Russell L. de Jesus, Kevin Lawrence M. School of Graduate Studies Mapua University 658 Muralla St. Intramuros Manila1002 Philippines School of Civil Environmental and Geological Engineering Mapua University 658 Muralla St. Intramuros Manila1002 Philippines Information and Technology Department University of Technology and Applied Sciences P. O. Box 74 Al Khuwair Muscat133 Oman Department of Civil Engineering College of Engineering FEU Institute of Technology P. Paredes St. Sampaloc Manila1015 Philippines

ISBN: (纸本)9789819754762

A crucial component of growth in infrastructure is estimating construction costs (CC) for pipelaying projects (PP) related to water distribution networks, which guarantees the effective and long-term provision of safe drinking water to communities. In this paper, an artificial neural network (ANN) and multiple linear regression (MLR) model was developed for predicting construction cost for pipelaying projects. The governing model (GM) has a model structure of 9-20-1 (input-hidden-output) with an R = 0.99992. The findings revealed that the ANN-based network was 13.127 times better than the MLR model, based on its MAPE of 3.214 and 42.194%, for ANN and MLR, respectively. The best network also has the lowest Akaike Information Criterion (AIC) among the simulated network structures indicating that it is the best network. The relative importance (RI) of the independent variables including the length, diameter, material type, hydrotesting works, disinfection works, demolition works, restoration works, duration delay, and liquidated damages were calculated utilizing the Garson’s algorithm (GA). It was seen using GA to compute the relative importance of each parameter that the order of influence is seen as restoration works (RW) > length > demolition works (DeW) > material type (MT) > diameter > disinfection works (DiW) > hydrotesting works (HW) > duration delay (D%) > liquidated damages (LD) wherein the restoration works is the most influential parameter. The findings of the study could be used as a reference for better planning and managing pipelaying project activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning 15

Edge-Cloud Cooperation for DNN Inference via Reinforcement L...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Zhang, Tinghao Li, Zhijun Chen, Yongrui Lam, Kwok-Yan Zhao, Jun Nanyang Technological University School of Computer Science and Engineering Singapore Harbin Institute of Technology School of Computer Science and Technology Harbin China University of Chinese Academy of Sciences Department of Electronic and Communication Engineering Beijing China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accuracy while maintaining low inference latency. To this end, we deploy a lightweight model on the edge and a heavyweight model on the cloud. A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model. Moreover, a supervised learning (SL)-based offloading strategy is applied to determine whether the sample should be processed on the edge or on the cloud. Our method is implemented on real hardware and tested on multiple datasets. The experimental results show that (1) The sizes of the lightweight models obtained by RL-based DNN compression are up to 87.6% smaller than those obtained by the baseline method;(2) SL-based offloading strategy makes correct offloading decisions in most cases;(3) Our method reduces up to 78.8% inference latency and achieves higher accuracy compared with the cloud-only strategy. © 2022 IEEE.

关键词： Deep neural networks

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Atmosphere-dependent combustion of Ganoderma lucidum biomass toward its enhanced transformability into green energy

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Environmental Science and Pollution Research 2024年第29期31卷 42372-42387页

作者： Cao, Hanlin Zhan, Hongda Qi, Jianying Lin, Sen Ren, Mingzhong Liang, Jiayu Evrendilek, Fatih He, Yao Liu, Jingyong Technical Centre for Soil Agriculture and Rural Ecology and Environment Ministry of Ecology and Environment Beijing100012 China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and Engineering Guangdong University of Technology Guangzhou510006 China Ministry of Ecology and Environment South China Institute of Environmental Sciences Guangzhou510275 China Department of Electrical and Computer Engineering College of Engineering Boston University BostonMA02215 United States

Globally, the circular efficiency of biomass resources has become a priority due to the depletion and negative environmental impacts of fossil fuels. This study aimed to quantify the atmosphere-dependent combustion of Ganoderma lucidum (GL) biomass and its thermodynamic and kinetic parameters toward enhancing its circularity and transformability characteristics. The GL combustion occurred in the three stages of moisture removal, volatile release, and coke combustion. Combustion performance characteristics were more favorable in the N2/O2 atmosphere than in the CO2/O2 atmosphere under the same heating rates. The rising heating rate facilitated the release of volatiles. According to the model-free methods of Ozawa-Flynn-Wall and Kissinger–Akahira–Sunose, the activation energies essential for the primary reaction were 283.09 kJ/mol and 288.28 kJ/mol in the N2/O2 atmosphere and 233.09 kJ/mol and 235.64 kJ/mol in the CO2/O2 atmosphere. The gaseous products of the GL combustion included CH4, H2O, C = O, CO, CO2, NH3, C = C, and C–O(H). Ash prepared in both atmospheres exhibited a tendency for slag formation, with oxy-fuel combustion lowering its risk. This study thus provides a theoretical and practical basis for transforming GL residues into a sustainable energy source. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Combustion

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Khilenko, Volodymyr Bielokha, Halyna Denysiuk, Serhii Stepanov, Oleksii Kotuliak, Ivan Slovak University of Technology in Bratislava Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Bratislava Slovakia National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic Institute" Power Supply Department Kyiv Ukraine National University of Life and Environmental Sciences Ukr Article Acdsa Department of Information Technologies Khilenko ID 414 Kyiv09-02-2024 Ukraine

ISBN: (纸本)9798350394528

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used. © 2024 IEEE.

关键词： Distributed power generation

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Soliton solutions for time fractional ocean engineering models with Beta derivative

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Journal of Ocean engineering and Science 2022年第5期7卷 444-448页

作者： Ibrahim Yalçınkaya Hijaz Ahmad Orkun Tasbozan Ali Kurt Department of Mathematics and Computer Sciences Faculty of ScienceNecmettin Erbakan UniversityKonyaTurkey Department of Basic Sciences University of Engineering and TechnologyPeshawarPakistan Department of Mathematics Faculty of Science and LiteratureHatay Mustafa Kemal UniversityHatayTurkey Department of Mathematics Faculty of Science and LiteraturePamukkale UniversityDenizliTurkey

In this study,the authors obtained the soliton and periodic wave solutions for time fractional symmetric regularized long wave equation(SRLW)and Ostrovsky equation(OE)both arising as a model in ocean *** this aim modified extended tanh-function(mETF)is *** using this method,chain rule is employed to turn fractional nonlinear partial differential equation into the nonlinear ordinary differential equation in integer *** to the chain rule,there is no further requirement for any normalization or *** derivative which involves fractional term is used in considered mathematical *** the exact solutions of these equations is very important for knowing the wave behavior in ocean engineering models.

关键词： Symmetric regularized long wave equation Beta derivative Ostrovsky equation Analytical solution Soliton solutions Periodic wave solution

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A Prescribed Paper Less Clinic to view the Patient's Documents 1

A Prescribed Paper Less Clinic to view the Patient's Documen...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： MacHap, Kamalakannan Prabhu, M. Ramkumar Rajalingam, A. Charulatha, G. Saranya, R. Renukadevi, S. School of Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Peri Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Shinas Oman R. M K. College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350397840

Diseases are a significant aspect of human beings in modern times. It plays a vital role in a safe life in the present era. At the same time, registration, missing medication letter, scans, etc., is among the most significant issues when interacting with physicians. Here it has to overcome these problems with Smartphone app control with user engagement innovations for the wellness climate. The Rx List Pill Identifier Tool recognizes generic, OTC, and marketed medicines by coloring pill, size, type, and medication print. Align and quickly identify the drugs with the images in the medication imprint (pill ID). In addition, the health details of patients, such as ECG, temperature, and BP, which are provided for reference purposes to cloud stocking, can be stored. The existing frameworks' outcome is to give guidance for transforming these traditional health services into lean systems that will aid in the creation of a poor hospital with satisfied customers. The centralized medical complex skin temperature tracking system comprises the access point and various control terminals, comprising a power supply, thermal signal array, ESP8266 transmission and reception, display, alarm, and motion sensor, among other things. The STM32 heart controls and operates the software. During operation, the host delivers the signal with the necessary addressing data. © 2022 IEEE.

关键词： Internet of things

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A Scalable Design Approach for State Propagation in Serverless Workflow 3

A Scalable Design Approach for State Propagation in Serverle...

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3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Bharti, Urmil Goel, Anita Gupta, S.C. Shaheed Rajguru College of Applied Sciences for Women University of Delhi Department of Computer Science Delhi India Dyal Singh College University of Delhi Department of Computer Science Delhi India Indian Institute of Technology Department of Computer Science Delhi India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The serverless Function-as-a-Service offerings lack state management support;therefore, it must be handled at application-level. In this paper, we propose a scalable design approach that simplifies development of workflows that require sharing of ephemeral intermediate data. Our design uses object serialization/deserialization with cloud object storage to share state across functions. It provides a mechanism for fine-grained support for state propagation and synchronization in a serverless workflow. This solution is cost-effective and efficient as it does not depend on any external database or cache for state management. The design has been validated by implementing 'Word Count'- a classic MapReduce use case. Our results show that the proposed scalable design can process input of any size and can handle state propagation in complex serverless workflow. © 2022 IEEE.

关键词： Digital storage

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Innovative computational techniques for DSSCs using machine learning: a review

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Discover Electronics 2024年第1期1卷 1-8页

作者： Yadav, Varsha Bhatnagar, Rahul Kumar, Upendra School of Applied Sciences Shri Venkateshwara University Gajraula (Amroha) India School of Engineering and Technology Shri Venkateshwara University Gajraula (Amroha) India Advanced Functional Materials Laboratory Department of Applied Sciences Indian Institute of Information Technology Allahabad prayagraj India

Solar energy, a highly abundant renewable resource, can be efficiently harnessed using Dye-Sensitized Solar Cells (DSSCs). DSSCs, known for their clean energy production, low cost, and simple fabrication, have reached photovoltaic conversion efficiencies exceeding 15% due to advancements in their components. However, optimizing DSSC design and performance through traditional experimental methods is time-consuming and expensive. This study explores integrating machine learning (ML) techniques to enhance DSSC efficiency, offering a technologically advanced method for identifying optimal materials and designs. By reviewing recent advancements in ML applications in DSSCs, the study highlights the process of implementing ML—from defining research objectives and datasets to selecting appropriate ML models and evaluating outcomes. Commonly used ML algorithms, such as Decision Tree (DT), Random Forest (RF), and Convolutional Neural Networks (CNN), have shown promise in predicting photovoltaic characteristics and material properties and optimizing design parameters. The application of ML in DSSCs has demonstrated high accuracy in predicting factors like power conversion efficiency (PCE), optical properties, and structural characteristics. This paper comprehensively reviews a recent research study published on the recent scenario, identifying key features and ML models that enhance DSSC performance. The findings underscore the potential of ML in accelerating DSSC development, despite the relatively lower number of studies compared to other solar technologies. ML emerges as a crucial tool for advancing DSSC efficiency and making it a viable alternative to renewable energy technologies.

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Effect of feature and sampling ratio on tool wear classification during boring operation using tree-based algorithms

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International Journal of Vehicle Information and Communication Systems 2022年第1期7卷 51-63页

作者： Surendar, S. Akshay, E. Elangovan, M. Vijayakumar, V. Department of Mechanical Engineering Velalar College of Engineering and Technology Tamilnadu Erode India School of Building and Mechanical Sciences VIT University Chennai Campus Tamilnadu India Anna University Coimbatore Tamilnadu India School of Computer Science and Engineering The University of New South Wales Sydney Australia

Tool condition monitoring (TCM) system is used to predict the tool wear during the machining process. The predominant wear is the flank wear which influences the surface roughness of the workpiece. The quantum of flank wear is to be ascertained so that a decision could be made whether the time has come for the insert to be replaced. Although the wear is continuous, it may be divided into three stages and may be classified as to which stage the tool wear falls into. Wear prediction may be carried out by extracting information from the vibration signals acquired during machining and interpreting them using machine learning. This paper focuses on monitoring the uncoated carbide tooltip during boring operation using tree based classifier algorithms such as random forest, J48, logistic model tree and gradient booted tree, in order to study the effect of feature and sampling ratio on tool wear classification when tree-based algorithms are used. Also, the statistical features and histogram features were compared for various cutting tool conditions to explore a better classifier-feature combination. © 2022 Inderscience Enterprises Ltd.

关键词： Wear of materials

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