Advancement in the field of computational algorithms diverted the focus of researchers to use these algorithms in the preservation of Visual Art Architecture. There are few research literature works available in this field, but these become the base for further future research work in the better advancements in the preservation of architecture. These few related works are listed in the paper. The paper focuses on the main outcomes of the available literature and findings for future possibilities in the field. The research works of these researchers were classified, described, analyzed, and compared in the different sections of the paper. The research was classified into three proposed classes based on similarities and dissimilarities among the research works, which are further classified into sub-class based on some special parameter. The comparison of these research works was done based on parameters proposed class, Algorithm used in the research, common evaluation matrix Classification Accuracy.
Ball bearing is used to provide free rotation around a fixed axis. Various kinds of faults exist in the bearing such as inner race fault, outer race fault and ball race fault. One very effective method to diagnose the bearing fault is vibration signal analysis. Empirical mode decomposition (EMD) has been used for ball-bearing fault diagnosis in mechanical systems using vibration signal analysis. Classification of the ball-bearing fault has always been a challenging task. Various classification schemes such as Extreme learning machine (ELM), K-means Clustering, and Support vector machine (SVM), have been reported in the literature for ball-bearing fault classification. SVM is restricted by multiclass classification efficiency, and ELM is restricted by the longer training. In this paper, the entropy analysis of the wavelet coefficient obtained from the third level decomposition of the residue signal (obtained after subtracting the highest frequency component from the raw signal) has been done for ball-bearing fault classification. A comparative analysis of the wavelet coefficient based on entropy measurement has been presented here. High fault classification accuracy has been achieved using the proposed method for the detection of ball-bearing fault. Shannon entropy, Average Shannon entropy, and Renyi’s entropy are parameters for the justification of the proposed approach. The result shows the best wavelet to be chosen among the available discrete wavelet based on various entropy measurements.
Owing to its use of solar radiation and ambient energy, solar based heat pump water heaters are environmentally friendly. Several recommendations for system optimization are offered based upon the experimental study and thermodynamics analysis to enhance the efficiency of the model. The goal of this work was to create and maintain a solar based heat pump prototype as well as to propose a comprehensive theoretical examination into the social-economic and energy efficiency of a new solar based heat pumping (SHP) model for water heater application. The ultimate focus of the performance evaluation and interpretation is to evaluate the viability and logical consistency of deploying a solar-based heat pump as an auxiliary water heating mechanism during local weather scenarios. Subsequently, a modelling approach is created for optimizing the performance. Moreover, the suggested system’s performance characteristics are assessed using the Solar Collector Efficiency (SCE) and Coefficient of Performance (COP). The optimal case’s simulation results demonstrate that the solar-based heat pump’s average COP is about 9.0. The comfortable level of temperature could be accomplished when a pleasant indoor temperature reaches 92.6 percent of the whole heating season. Traditional air source heat pump systems may not be as promising for domestic use in cold temperature areas as the optimal proposed system does.
The high gain, high speed/data rate, high capacity, and beamforming antennas are required for the present generation of mobile and wireless applications to satisfy the exponentially growing demands of the users. This paper presents low mutual coupling multiple input-multiple output (MIMO) array antenna for millimetre-wave (mmw) application. The MIMO-array beamforming antenna with 2:1 VSWR band is proposed for 28.0 GHz and covers 27.04–28.35 GHz frequency band, which is suitable for mm-wave n261 5G-band which cover the frequency range from 27.5–28.35 GHz. It consists of 2×12 antenna array elements and the prototype is designed on low loss Rogers Duroid 5880 substrate of size 51.45×36.87mm2. The beamforming MIMO antenna covers ±200 main lobe directions. The mutual coupling at the MIMO-array ports is less than 28.0 dB. The radiation efficiency and the gain in the presented band are more than 93.0% and more than 13.99 dBi. The ECC in the presented frequency band is ≤10-4, which is one of the advantages of the proposed design. The design covers indoor and outdoor Gaussian applications, and has 1.31 GHz TARC active bandwidth. It has 4.65% simulated and 4.73% measured fractional bandwidths.
Automatic identification of abnormal and irregular heart rhythms is necessary to reduce mortality. Tachyarrhythmia is a type of abnormally fast heartbeat that can be detected using electrocardiogram (ECG) signals. In the elderly, life-threatening tachyarrhythmia such as ventricular fibrillation (VFIB), atrial fibrillation (AFIB), and atrial flutter (AFL) can lead to sudden cardiac arrest. Here, we present a hybrid deep learning (HDL) model for automatic identification of tachyarrhythmia rhythms from heart rate variability (HRV) datasets based on a one-dimensional convolution neural network (1D CNN) and a long-term short-term memory (LSTM) model. In this study, we used the HRV database with five-second windows as input data for our HDL model. Four different statistical parameters have been used to determine the model efficiency: The average accuracy is 99.19%, the average precision is 91.75%, the recall is 93.63%, and the F1 score is 92.71%. The overall accuracy of the experiment was 98.4%. This model outperformed other state-of-the-art models. As a result, this method can be useful in clinical systems of cardiological care.KeywordsAFIBAFLVFIBHRVCNNLSTM
- This paper proposes a dual-band RF energy harvester to power biomedical wireless devices for various health applications. Biomedical devices may be implantable medical devices or wearable devices. The proposed system's novelty is that it can work in widespread operating power ranges with good conversion efficiencies. The Pi impedance matching network is used in the projected dual-band rectifier that is practicable for RF energy harvesting within the existing band of frequencies. The proposed antenna is simulated and designed using CST MW Studio 2018, while for the designing and simulation of the rectifier, ADS 2020 is used. Also, for the DC pass filter and matching network, ADS 2020 is used. For fabrication, an FR-4 lossy substrate having a dielectric constant of 4.4 and dielectric height of 1.6 mm is used. The projected result shows that the conversion efficiency is greater than 50% (capable of 78%), with an input power range of 8.5–10 dBm. At an input power of 8 dBm, the typical DC voltage was increased to 1.8 V. With the use of matching circuits, the maximum conversion efficiency is 78%, while without a matching circuit, the maximum conversion efficiency is only 37%. The matching circuit was critical for conversion efficiency.
Nature-inspired and metaphor-less population-based computational techniques have gained tremendous importance for solving real-world constrained optimization complications during the last twenty years. This is due to their unconventional direct search mechanism, ease of application and non-dependence on the mathematical nature of objective function. Amongst the various engineering optimization problems, the economic dispatch (ED) is one of the most important problems which has been addressed using a large number of these techniques. In power system operation, ED has a special place and therefore this topic has received immense attention from researchers. With changing operational philosophies and technological advancements, the ED problem formulation has undergone many changes over the years. Beginning with simple and approximate models, various different practical constraints were integrated into the classical ED problem over time, the latest being integration and modelling of the renewable energy (RE) sources. The paper presents a review of different metaheuristic techniques proposed for various types of ED problems with renewable energy integration based on the last decade.
Particulate matter has a significantly larger impact on human health than other toxins which makes air pollution a highly serious problem. The air quality of a given region can be utilized as a primary determinant of the pollution index, as well as how well the industries and population are controlled. With the development of industries, monitoring urban air quality has become a persistent issue. At the same time, the crucial effect of air pollution on individuals’ healthiness and the environment and monitoring air quality is becoming gradually important, mainly in urban areas. Several computing methods have been studied and compared to verify the accurateness of air quality forecasting requirements to date, ranging from machine learning to deep learning. This paper introduced a deep learning air quality forecasting approach based on the convolutional bidirectional long short-term memory (CBLSTM) model for PM 2.5, which combines 1D convolution and bidirectional LSTM neural networks. The experiment findings demonstrate that the suggested approach outperforms the LSTM, CBLSTM, and CBGRU comparison models and achieves a high accuracy rate (MAE = 6.8 and RMSE = 10.2).
The renewable energy sources are clean energy sources, and their role and contribution are increasing day by day. In this paper, optimal sizing has been carried out for stand-alone hybrid energy system (HES) with solar PV, wind turbine (WT), diesel generator (DG), and energy storage. Black widow optimization (BWO) is one of the recent and powerful algorithms which is implemented for finding the optimal sizing of HES through energy management based on sample day load data using annualized model. The main goal of this study is to minimize the cost of energy such that all practical constraints are satisfied. A comparison of BWO has also been carried out with differential evolutionary (DE), particle swarm optimization (PSO), and a traditional solver (TS). It is concluded that the BWO performs better than the other optimization technique. Different configurations were tested for finding the best combination of units to be installed.KeywordsHybrid energy system (HES)Black widow optimization (BWO)Cost of energy (COE)Differential evolution (DE)Traditional solver (TS)Particle swarm optimization (PSO)
Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.
Erythemato-squamous diseases (ESD) diagnosis is a significant challenge in dermatology. It is divided into six categories. Artificial intelligence models have been applied to categorize these categories. Artificial intelligent models are black boxes in nature. The objective of this study is to unbox the black-box behavior and interpret the decision-making. Random Forest and XGBoost models are applied on a standard dataset with SHAP value to get interpretability and causability of decision. The Random Forest model had a classification accuracy of 98.21%. Integration of explainability increase the transparency of result and identify the root cause of the disease in the subject. A comprehensive quantitative study will help to adopt artificial intelligence in healthcare with ethical issues like transparency, causability, and interpretability of diagnosis.
Load dispatch is an indispensable part of power system operation. Economic Dispatch (ED) and Combined Economic Emission Dispatch (CEED) are the standard complex contained benchmark used for accessing the potential of new optimization algorithms. The complexity of optimization problems has increased with time due to several factors like the type of generating units (dispatchable or non-dispatchable), their mathematical modelling (probabilistic or deterministic), internal characteristics (convex or non-convex), forecasted load demand and others. Metaheuristic optimization techniques have been able to handle these constraints appreciably due to their stochastic attributes. This paper attempts to explore the performance of a novel bio-inspired algorithm named as Manta Ray Foraging Optimizer (MRFO) for ED of a microgrid (MG) and CEED problems for a hybrid thermal solar PV system respectively. Both system considered for analysis have different scalability in terms of power demand. The performance of MRFO is compared with classical metaheuristic as well as new generation metaheuristic algorithms. Box plot analysis is also performed to compare the results statistically. Finally, it can be concluded that the performance of MRFO has outperformed the performance of other algorithms and has a future scope to entertain more practical and real-life engineering problems.
Vibration compensation in track displacement monitoring is explored by using rail image feature points. The key point is to compare the image of the rail to be tested with the image of the standard rail. A downstream section of Metro Line A is selected for real time analysis of image. The sampling frequency of the detection system is 0.5m/point, and the driving speed of the detection vehicle is about 40km/h, take the distance of 100m between K23+000‐K22+900 of kilometer mark, total 200 points, and analyze the image data by grey scale conversion concept. In order to verify the accuracy of the vibration compensation data, one point is taken at an interval of 5m, and a total of 20 points are measured, and compare and analyze with dynamic real‐time detection data. After vibration compensation, the geometric parameters of the contact rail are closer to the expected value, and the maximum error can be controlled within …
This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.
COVID-19 is an infectious and contagious disease caused by the new coronavirus. The total number of cases is over 19 million and continues to grow. A common symptom noticed among COVID-19 patients is lung infection that results in breathlessness, and the lack of essential resources such as testing, oxygen, and ventilators enhances its severity. Chest X-ray can be used to design and develop a COVID-19 detection mechanism for a quicker diagnosis using AI and machine learning techniques. Due to this silver lining, various new COVID-19 detection techniques and prediction models have been introduced in recent times based on chest radiography images. However, due to a high level of unpredictability and the absence of essential data, standard models have showcased low efficiency and also suffer from overheads and complexities. This paper proposes a model fine tuning transfer learning-coronavirus 19 (Ftl-CoV19) for COVID-19 detection through chest X-rays, which embraces the ideas of transfer learning in pretrained VGG16 model with including combination of convolution, max pooling, and dense layer at different stages of model. Ftl-CoV19 reported promising experimental results; it observed training and validation accuracy of 98.82% and 99.27% with precision of 100%, recall of 98%, and F1 score of 99%. These results outperformed other conventional state of arts such as CNN, ResNet50, InceptionV3, and Xception.
Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA‐based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye‐1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.
In India, PV integration with the low-voltage distribution system is growing rapidly. PV systems require embodied energy for their construction; additionally, they give energy support to the grid and also have an impact on grid parameters including Power Factor (PF), Maximum Demand (MD) and capacitive reactive-power (Q) requirement. Analysing the impact of PV integration on grid supply parameters, including performance and embodied energy-based-energy metrics is the current need. The study is performed by real-time data monitoring on 100-kWp grid-integrated PV in a composite environment. The annual average final yield:(3.76 kWh/kWp/day), system efficiency:(10.89%), performance ratio:(72.15%), energy pay-back time:(6.19 years), electricity production-factor:(4.04) and life cycle conversion efficiency:(12.50%) suggest that the system is energy efficient. The integration of PV with the grid has reduced the average PF and MD to 0.91 (lagging) and 133.63 kVA, respectively, whereas Q has increased to 28.67 kVAR. With a Levelised-cost-of-electricity:(LCOE) of Rs 6.19, the system has a pay-back of 5.29 years.
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