Photovoltaic(PV)arrays are usually installed in open areas;hence,they are vulnerable to lightning strikes that can result in cell degradation,complete damage,service disruption,and increased maintenance *** a result,i...
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Photovoltaic(PV)arrays are usually installed in open areas;hence,they are vulnerable to lightning strikes that can result in cell degradation,complete damage,service disruption,and increased maintenance *** a result,it is imperative to develop an effective and efficient lightning protection system by evaluating the transient behaviour of PV arrays during lightning *** aim is to evaluate the transient analysis of large-scale PV systems when subjected to lightning strikes using the finite difference time domain(FDTD)*** overvoltages are calculated at various points within the mounting *** optimise the FDTD method's execution time and make it more suitable for less powerful hardware,a variable cell size approach is ***,larger cell dimensions are used in the earthing system and smaller cell dimensions are used in the mounting *** FDTD method is utilised to calculate the temporal variation of transient overvoltages for large-scale PV systems under different scenarios,including variations in the striking point,soil resistivity,and the presence of a metal *** results indicate that the highest transient overvoltages occur at the striking point,and these values increase with the presence of a PV metal frame as well as with higher soil ***,a comparison is performed between the overvoltage results obtained from the FDTD approach and the partial element equivalent circuit(PEEC)method at the four corner points of the mounting systems to demonstrate the superior accuracy of the FDTD ***,a laboratory experiment is conducted on a small-scale PV system to validate the simulation *** calculated overvoltages obtained from the FDTD and PEEC methods are compared with the measured values,yielding a mean absolute error of 5%and 11%for the FDTD and PEEC methods,respectively,thereby confirming the accuracy of the FDTD simulation model.
The underwater environment is complex and diverse, making it challenging to locate aquatic organisms accurately. The precise identification of underwater animals is crucial for ecological research and fisheries manage...
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This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques...
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With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Mac...
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With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human ***,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].
Human activity recognition is a crucial domain in computerscience and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, ...
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Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;theref...
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Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.
In recent years, infrared target detection has played a crucial role in intelligent transportation and assisted driving. Addressing the current issues of low detection accuracy, poor robustness, and missed detections ...
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In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for u...
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Owing to the significant potential of alkalin seawater electrolysis for converting surplus power into eco friendly hydrogen fuel,we developed bifunctional elec trodes that integrate low-crystalline NiFe LDHs and amorp...
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Owing to the significant potential of alkalin seawater electrolysis for converting surplus power into eco friendly hydrogen fuel,we developed bifunctional elec trodes that integrate low-crystalline NiFe LDHs and amorphous NiFe alloy on a Ni foam(NF)substrate to enhance this *** by the battery-like charac teristics of NiFe LDHs,an anti-corrosive and active oute layer of NiFe^(vac)OOH continuously forms over time in th hybrid on the anode for the oxygen evolution reaction(OER),effectively mitigating powder shedding caused by corrosion induced by multiple anions in *** while,the strong bond between the hybrid and the NF substrate maintains intact hybrid coatings to ensure a rel atively high overall conductivity of the electrodes,signif icantly reducing the negative effects of structura degradation during the OER and hydrogen evolution reaction(HER),as well as the accumulation of contami nants on the electrode *** long-term tests,thes bifunctionalhybridelectrodesmaintained stable performance,even at a high current density o500 mA·cm^(-2).The cell voltage increased by only 88 m V over 1000 h to 1.970 V during saline electrolysis and by103 mV over 500 h to 2.062 V during seawater electroly ***,this study provides valuable insights into efficient and stable seawater electrolysis using NiFe LDHs–NiFe alloy hybrids.
作者：
Pawar, AdeshGourshettiwar, PalashGote, Pradnyawant M.Choudhary, Vishal KumarYesankar, PrajyotKhadse, Shrikant
Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha India
Department of Computer Science and Design Faculty of Engineering and Technology Maharashtra Wardha India
Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Maharashtra Wardha India
Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Techno...
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