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Degradation of PV modules can cause excessive overheating which results in a reduced power output and eventually failure of solar panel. To maintain the long term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. This paper provides a comprehensive algorithm for fault diagnosis in solar modules using infrared thermography. Infrared Thermography (IRT) is a reliable, non-destructive, fast and cost effective technique which is widely used to identify where and how faults occurred in an electrical installation. Infrared images were used for condition monitoring of solar modules and fuzzy logic have been used to incorporate intelligent classification of faults. An automatic approach has been suggested for fault detection, classification and analysis. IR images were acquired using an IR camera. To have an estimation of thermal condition of PV module, the faulty panel images were compared to a healthy PV module thermal image. A fuzzy rule-base was used to classify faults automatically. Maintenance actions have been advised based on type of faults. [ABSTRACT FROM AUTHOR]

The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning. [ABSTRACT FROM AUTHOR]

Infrared thermography is a non destructive temperature measuring technique, widely used in agriculture, process industries, manufacturing, pharmaceutical industries, and construction industries to detect anomalies, to predict possible faults, or to check the quality of the object. In this paper, two systems, real time and off line, are proposed to monitor the temperature variations and analyze hot regions in the electrical assets using infrared thermograms. This novel technique helps to prevent the electrical assets before any catastrophe would happen in the future. The color based segmentation technique is used to blotch hot regions in the thermograms of electrical systems. A redness area based algorithm is also proposed to analyze the hot regions and also to estimate rate of change of hotness in electrical assets for early detection and prediction of faults. [ABSTRACT FROM AUTHOR]

Among various types of skin diseases, skin cancer is the deadliest form of the disease. This paper classifies seven types of skin diseases: Actinic keratosis and intraepithelial carcinoma, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanoma, Melanocytic type, and Vascular lesions. The primary objective of this paper is to evaluate the performance of these deep learning networks on skin lesion images. The lesion classification is implemented through transfer learning on fourteen deep learning networks: AlexNet, GoogleNet, ResNet50, VGG16, VGG19, ResNet101, InceptionV3, InceptionResNetV2, SqueezeNet, DenseNet201, ResNet18, MobileNetV2, ShuffleNet and NasNetMobile. The dataset used for these experiments are from ISIC 2018 of about 10,154 images. The results show that DenseNet201 performs best with 0.825 accuracy and improves skin lesion classification under multiple diseases. The proposed work shows the various parameters, including the accuracy of all fourteen deep learning networks, which helped build an efficient automated classification model for multiple skin lesions. [ABSTRACT FROM AUTHOR]

The HNN is often used as a human-like associative memory (AM). Nonetheless, HNNs suffered from limited noise tolerance and limited storage capacity. However, unfortunately, Hopfield neural networks suffered from limited tolerance to noise and limited storage capacity. This study proposes a novel approach for overcoming these limitations using the concept of multiple connections architecture in a Hopfield neural network with an energy function and Hamming distance (HD). In this architecture, a single connection among neurons of the network is employed to store a pattern, resulting in an etalon array, and the collection of etalon arrays corresponding to each neuronal connection forms a weight matrix (also called connection matrix). This approach of storing a single pattern on a single set of connections reduces the chances of being trapped in local minima and increases storage capacity because more patterns are stored in the network as the number of connections increases. The Lyapunov energy function and the Hamming distance concept are utilized to determine the testing pattern’s proximity to one of the stored patterns for perfect recall. Several experiments are conducted to compare the recall success rate and storage capacity of handwritten character images to those of existing methods. Experiments using multiple connections Hopfield neural network with hamming distance and Lyapunov energy function demonstrate promising results in comparison to existing methods. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the diseases which lead to death if not detected at an early stage. Computer-aided detection and diagnosis systems are designed for its early diagnosis which may prevent biopsy and use of dermoscopic tools. Numerous researches have considered this problem and achieved good results. In automatic diagnosis of skin cancer through computer-aided system, feature extraction and reduction plays an important role. The purpose of this research is to develop computer-aided detection and diagnosis systems for classifying a lesion into cancer or non-cancer owing to the usage of precise feature extraction technique. This paper proposed the fusion of bag-of-feature method with speeded up robust features for feature extraction and quadratic support vector machine for classification. The proposed method shows the accuracy of 85.7%, sensitivity of 100%, specificity of 60% and training time of 0.8507 s in classifying the lesion. The result and analysis of experiments are done on the PH2 dataset of skin cancer. Our method improves performance accuracy with an increase of 3% than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with fault diagnosis of solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed. After reviewing relevant work, a monitoring tool is designed using thermography and artificial intelligent systems that allows the detection of various types of faults in PV modules and at the same time the designed tool aims to filter the nonsignificant anomalies. A neural network (NN) classifier is applied to the transfer characteristics (I‐V data) of the faulty PV module for the diagnosis which adapts multilayer perceptron (MLP) networks to identify the type and location of occurring faults. The Discrete wavelet transform (DWT) based signal processing technique is utilized in the feature extraction process to reduce the NN input size. The developed detection algorithm is adapted for 24/7 automated surveillance. For a given fault condition, the average fault detection time is observed to be <9 seconds, which is lower than the previous work done. The developed algorithm achieved 100% accuracy when tested on a predetermined fault data set. [ABSTRACT FROM AUTHOR]