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Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

computers, Materials & Continua 2021年第11期69卷 1709-1723页

作者： Rohit Samkria Mohammed Abd-Elnaby Rajesh Singh Anita Gehlot Mamoon Rashid Moustafa H.Aly Walid El-Shafai Project Engineer NWTFIndian Institute of TechnologyKanpurIndia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Electrical Engineering Lovely Professional UniversityJalandharIndia School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Department of Electronics and Communications Engineering College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria 1029Egypt Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’*** identify the fault of the PV arrays,there is a necessity to implement an automatic *** this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity *** component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum *** the LabVIEW environment,an automated monitoring system is *** automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface *** managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV *** MCU in the receiver section enables transmission of the fault status of PV arrays via Internet *** IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV *** dashboard of Blynk visualizes every array with the status.

关键词： Blynk app IoT LabVIEW node MCU PV array RF modem WSN

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Speech Measuring Following Semiconductor Feature for Acoustic using Biodegradable Material 3

Speech Measuring Following Semiconductor Feature for Acousti...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Upadhyay, Shrikant Kumar, Rakesh Kumar, Binod Kumari, Barkha Lakra, Ankita Mandal, Sampurna Department of Electronics and Communication Engineeing MLR Insitute of Technology Hyderabad India Wipro Engineering Edge Hyderabad India Facultyt of CSE & IT Jharkhand Rai University Ranchi India GH Raisoni College of Engineering and Management Pune India Faculty of Computing and IT Usha Martin Univerity Ranchi India Department of Computer Science and Engineering-Artifical Intelligence Brainware University Kolkata India

ISBN: (纸本)9798331527549

This research paper primarily focuses to identify the various feature removal presence used for addresser recognition and validation. The current template i.e. classical and typical are considered to examine its role in different acoustic surroundings and also try to measure feature removal in acoustic condition considering factors like jitters, noise, echo etc. which truly a factor that affect the speech performance and degrades the overall performance of any system. Effort has been put to inspect the attainment of various feature removal approach with the help of classifier considering different structures. The behavior of different feature extraction removal simulated using Hindi samples and it will be analysed in terms of efficiency and error rate. The result may be useful for suitable for existing as well as any proposed model by changing its classifiers. Speech of different ages as well groups are used to analysed and implementation using 16-bit TMS 320 C5X semiconductor devices with the feature extraction and algorithm may improve the overall efficiency of the proposed system. Bio-degradable material can also help to reduce the acoustic level at different of speaker identifications. The feature of semiconductor helps to understand and also add the improvements in proper selection of feature extraction that consume less time in evaluating the algorithm and provide better efficiency in different acoustic conditions. Biodegradable material has not used so far in various studies and biodegradable paly a very important role in acoustic condition as we are dealing with acoustic condition and it is very important to take care of acoustic to come up with better efficiency. © 2025 IEEE.

关键词： Semiconductor devices

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Fuzzy Decision-Making on Strategies for Maximizing the Utility of Electric Vehicles 3

Fuzzy Decision-Making on Strategies for Maximizing the Utili...

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3rd IEEE International Conference on Power electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Imran, Shaik Moinuddin Rameshan, N. Anand, M. Clement Joe Martin, Nivetha Tiwari, Mohit Mohan, Varun Sri Venkateswara Institute of Technology Electronics & Communication Engineering Andhra Pradesh Anantapur515001 India Srm Institute of Science and Technology Faculty of Engineering & Technology Department of Mathematics Vadapalani Campus Chennai600026 India Department of Mathematics Karnataka Bengaluru560052 India Department of Mathematics Tamil Nadu Karumathur625514 India Bharat Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi110063 India Sharda University Department of Mathematics Greater Noida201310 India

ISBN: (纸本)9798350387193

Environmental sustainability is one of the sustainable development goals and the invention of electric vehicles marks the beginning of alternatives to fuel-based vehicles. Electric vehicles have penetrated the global market to reduce carbon emissions and create a carbon-neutral community. Though these vehicles are environmentally friendly a large population of people in developing nations are still hesitant to purchase these vehicles as they are expensive. This research work proposes strategies for maximizing the utility of E-vehicles based on the obstructing factors using the multi-criteria decision-making method of the Fuzzy Base criterion method (FBCM). The strategies proposed in this paper are ranked based on the score values obtained using representations by trapezoidal fuzzy numbers. The consistency of the results is compared with other decision-making methods and the results obtained using FBCM were found to be more optimal. In this work, the special focus is made concerning developing nations. This paper also presents the extensive role of multi-criteria decision-making methods in designing optimal solutions to the problems of different dimensions associated with electric vehicles. © 2024 IEEE.

关键词： Sustainable development goals

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EEG-based Brain Wave Recognition using GRU and LSTM 5

EEG-based Brain Wave Recognition using GRU and LSTM

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Shashidhar, R. Tippannavar, Sanjay S Bhat, Krishnakumar Balachandra Sharma, Neha Rashid, Mamoon Rana, Arti JSS Science and Technology University Department of Electronics and Communication Engineering Mysuru India Chitkara University Institute of Engineering and Technology Department of Computer Science & Engineering Chitkara University Punjab Rajpura India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The human brain is made up of millions of neurons, each of which plays a crucial part in directing the behaviour of the human body in response to internal/external motor-impulses. These neurons will act as data conduits between the brain and the body. Studying brain signals or images may help us better understand the cognitive behaviour of the brain. Motor and sensory states including eye movement, lip movement, memory, concentration, hand clutching, and others may be used to depict human behaviour. These states are associated with certain signal frequencies, which aids in understanding the functional behaviour of complex brain structures. In this study, the characterization of EEG signals in connection to various bodily states is the main topic of study. It also talks about the experimental design used for the EEG analysis. On a dataset of physiological signals, in this case the AMIGOS dataset, including electrocardiogram and galvanic skin reaction, this study uses a deep convolutional neural network. In order to characterize a person's emotional state, these physiological signals are combined with the dataset's arousal and valence data. In addition, a tool for detecting emotions based on traditional machine learning techniques is provided in order to extract the properties of physiological data in the time, frequency, and nonlinearity domains. For autonomous feature extraction of physiological inputs in this application, a convolutional neural network is used. Fully connected network layers are then used to predict emotions. Compared to the original authors of this dataset, the experimental results on the AMIGOS dataset show that the approach suggested in this study provides more accurate emotional state categorization. © 2022 IEEE.

关键词： Convolution

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A Joint Data Compression and Time-Delay Estimation Distributed Systems via Extremum Encoding

A Joint Data Compression and Time-Delay Estimation Distribut...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amir Weiss Yuval Kochman Gregory W. Wornell Faculty of Engineering Bar-Ilan University School of Computer Science and Engineering The Hebrew University of Jerusalem Research Laboratory of Electronics Massachusetts Institute of Technology

Motivated by the proliferation of mobile devices, we consider a basic form of the ubiquitous problem of time-delay estimation (TDE), but with communication constraints between two non co-located sensors. In this setting, when joint processing of the received signals is not possible, a compression technique that is tailored to TDE is desirable. For our basic TDE formulation, we develop such a joint compression-estimation strategy based on the notion of what we term "extremum encoding", whereby we send the index of the maximum of a finite-length time-series from one sensor to another. Subsequent joint processing of the encoded message with locally observed data gives rise to our proposed time-delay "maximum-index"-based estimator. We derive an exponentially tight upper bound on its error probability, establishing its consistency with respect to the number of transmitted bits. We further validate our analysis via simulations, and comment on potential extensions and generalizations of the basic methodology.

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Efficiency analysis of deep learning-based object detection for safe human robot collaboration

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IFAC-PapersOnLine 2024年第19期58卷 1024-1029页

作者： Adam Dudek Justyna Patalas-Maliszewska Krzysztof Rokosz Faculty of Technical Science University of Applied Sciences in Nysa 48-300 Nysa Poland University of Zielona Gora Institute of Mechanical Engineering Poland Koszalin University of Technology Faculty of Electronics and Computer Science

Nowadays, deep-learning object-based detection is very often used for predicting and ensuring safety, in workspaces shared by human operators and robots. The main challenge is to evaluate the accuracy of such models depending on the acquired data, selected Artificial Intelligence architecture and on the relevant parameters for achieving the best efficiency. In this paper, the efficiency of detecting deep learning-based objects, applying the Region-Based CNN (YOLOv8 Tiny) was analysed for detecting objects within human robot collaboration (HRC).The relevant parameters were selected in each area discussed and the efficiency of the YOLOv8 Tiny applied for the object’s recognition was analysed. The research results indicate that the techniques for detecting objects, in HRC, applying YOLOv8 Tiny for interferences in the recorded material analysed the most was achieved by some 90%, however, in the context of the analysis of smoke disruption this was insufficient.

关键词： deep-learning object detection safety human robot collaboration (HRC) real data the efficiency a YOLOv8 Tiny

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Exploring the Impact of Error Type Features Integration on Transformer-based Myanmar Spelling Correction

Exploring the Impact of Error Type Features Integration on T...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Ei Thandar Phyu Ye Kyaw Thu Hutchatai Chanlekha Kotaro Funakoshi Thepchai Supnithi Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Spelling correction is a fundamental aspect of natural language processing systems, essential for ensuring the accuracy and fluency of text-based applications. In the context of neural machine translation based spelling correction, spelling errors pose significant challenges, particularly in low-resource language like Myanmar language where training data is limited. In this paper, the Myanmar spelling error-correction parallel corpus was developed through the collection of text from social media platforms. By learning the error patterns in social media text data, we categorized the Myanmar spelling mistakes into ten categories. We developed Myanmar spelling correction model using Transformer architecture and also investigated the potential improvement of our Transformer-based spelling correction by incorporating error type features as additional linguistic information. Evaluating the Transformer model with manually annotated error type labels in our developed corpus significantly enhances the F1-score from 75.91% to 86.71%, increases the GLEU score from 0.9648 to 0.9767, and decreases the word error rate from 3.3% to 2.0%. Additionally, we conducted a preliminary study on integrating our proposed correction model with an automatic error type classification model, employing a bidirectional Long Short-Term Memory. The findings demonstrate that integrating error type information into the Transformer architecture enhances the effectiveness of Myanmar spelling correction.

关键词： Social networking (online) Computational modeling Training data Neural machine translation computer architecture Linguistics Transformers

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Toward Realization of RIS-assisted Wireless Information and Power Transfer System: Implementation and Experiments

Toward Realization of RIS-assisted Wireless Information and ...

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International Symposium on Antennas and Propagation (ISAP)

作者： Je Hyeon Park Muhammad Miftahul Amri Nguyen Minh Tran Sung Woo Cho Su Hyun Kim Jina Lee Dong In Kim Kae Won Choi Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Faculty of Electronics and Telecommunications VNU - University of Engineering and Technology Hanoi Vietnam

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

This paper presents a series of studies on the implementation of reconfigurable intelligent surface (RIS) in wireless communication and power transfer systems. The discussed studies put together comprehensive RIS-assisted systems covering RIS unit cell design, testbed setup, practical algorithms, and experimental validations. The results of these experiments highlight the significant enhancement that RIS can bring to wireless network performance.

关键词： Wireless networks Prototypes Wireless power transfer Reconfigurable intelligent surfaces Antennas and propagation

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Sorting of Fresh and Damaged Apple Fruits using Machine Learning Approach

Sorting of Fresh and Damaged Apple Fruits using Machine Lear...

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Emerging technology (INCET), International Conference for

作者： Ujwala Ghodeswar Chetan Puri Sharvari Shingade Teena Waware Ajinkya Ladhe Tejas Durge Department of Electronics Engineering Yeshwantrao chavan College of Engineering Nagpur India Department of Computer Science & Design Faculty of Engineeering & Technology Wardha India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The research investigates how machine learning algorithms and Internet of Things (IoT) technologies might be combined to solve the crucial problem of separating fresh fruits from damaged apple fruits. Using Internet of Things (IoT) devices with sensors, the system collects extensive data on important characteristics, such as real-time apple picture analysis and available apple database. Convolutional neural networks (CNNs) and YOLO are two examples of sophisticated machine learning models that are used with this data to accurately classify the apples into different groups according to their quality. This initiative fosters enhanced efficiency, production, and profitability in the agricultural supply chain while providing a sustainable solution to the problems connected with fruit sorting. It represents a synergistic convergence of IoT, machine learning, and agriculture.

关键词： YOLO Training Machine learning algorithms Databases Supply chains Machine learning Production

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin Cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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