Showing papers in "Sadhana-academy Proceedings in Engineering Sciences in 2022"
TL;DR: In this paper , the concept of gH-subgradient and gHsubdifferential of convex interval-valued functions were introduced and analyzed, e.g., closeness, boundedness, chain rule, etc.
Abstract: In this article, the concepts of gH-subgradient and gH-subdifferential of interval-valued functions are illustrated. Several important characteristics of the gH-subdifferential of a convex interval-valued function, e.g., closeness, boundedness, chain rule, etc. are studied. Alongside, we prove that gH-subdifferential of a gH-differentiable convex interval-valued function contains only the gH-gradient. It is observed that the directional gH-derivative of a convex interval-valued function is the maximum of all the products between gH-subgradients and the direction. Importantly, we prove that a convex interval-valued function is gH-Lipschitz continuous if it has gH-subgradients at each point in its domain. Furthermore, relations between efficient solutions of an optimization problem with interval-valued function and its gH-subgradients are derived.
8 citations
TL;DR: This study inferred the promising results for a diagnostic system for retinal detachment with relatively high sensitivity and specificity for deep convolutional networks trained and tested on publically available datasets of RD and Non-RD fundus images.
7 citations
TL;DR: In this article , the authors compared CycleGAN and AttentionGAN for face aging task and concluded that overall CycleGAN has better performance than AttentionGAN, which consists of attention masks and content masks multiplied with the generated output in one domain to generate a highly realistic image in another domain.
Abstract: Recently, there is incredible progress in the arena of machine learning with generative adversarial network (GAN) methods. These methods tend to synthesize new data from input images that are highly realistic at the output. One of its applications in the image-to-image transformation way is the face aging task. In the face aging process, new face images are synthesized with the help of the input images and desired target images. Face aging can be beneficial in several domains such as in biometric systems for face recognition with age progression, in forensics for helping to find the missing children, in entertainment, and many more. Nowadays, several GANs are available for face aging applications and this paper focuses on the insight comparison among the frequently used image-to-image translation GANs which are CycleGAN (Cycle-Consistent Adversarial Network) and AttentionGAN (Attention-Guided Generative Adversarial Network). The first model (CycleGAN) comprises two generators, two discriminators, and converting an image from one domain to another without the need for paired images dataset. The second is AttentionGAN, which consists of attention masks and content masks multiplied with the generated output in one domain to generate a highly realistic image in another domain. For comparison, these two are trained on two dataset which is CelebA-HQ (CelebFaces Attributes high-quality dataset) and FFHQ (Flickr Faces HQ). Efficacy is evaluated quantitatively with identity preservation, five image quality assessment metrics, and qualitatively with a perceptual study on synthesized images, face aging signs, and robustness. It has been concluded that overall CycleGAN has better performance than AttentionGAN. In the future, a more critical comparison can be performed on the number of GANs for face aging applications.
5 citations
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TL;DR: In this article , the authors present a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing, which is done to maximize the profits of distribution companies that have used DG in their network to meet several advantages.
Abstract: The growing use of distributed generation (DG) at the distribution level has led to a change in the status of distribution networks from a passive network to an active network such as transmission systems. Therefore, transmission network pricing method such as nodal pricing could be used in the distribution network. DG connection to the distribution network affects bus nodal pricing. If the DG presence reduces losses and congestion in the distribution network, nodal pricing will also decrease. This paper presents a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing. This planning is done to maximize the profits of distribution companies that have used DG in their network to meet several advantages. The simulation was performed using the improved artificial bee colony algorithm (IABC). In the IABC algorithm, by exchanging the received information between bees according to Newton and gravity laws, it uses all this algorithm capacity to find the ideal answer by considering the constraints applied to the system. In most DG placement articles, network loads are assumed to be constant. Because loads are often sensitive to voltage and frequency, constant load analysis leads to inaccurate results. Therefore, in this paper, the proposed method is implemented on a 38-bus radial distribution system with a model of real loads sensitive to the voltage and frequency of the system, including residential, commercial, and industrial loads.
4 citations
TL;DR: This paper proposes a secure ID-based encryption scheme whose security depends on the newly discovered hard problems in the algebraic structure of group rings and shows that the proposed scheme is IND-ID-CPA secure and safe against the chosen ciphertext attack.
TL;DR: It is observed that the stopword removal generally improves mean average precision (MAP) significantly compared with the case when it is not done, and can be recommend, based on experiment, a number of stopwords for chosen Indian languages that are good enough from retrieval point of view.
TL;DR: In this article , the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020 were analyzed and compared.
Abstract: The present paper deals with the studies on the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020. The monthly average concentrations of the above pollutants observed at four monitoring stations in and around Nagpur city during January to December 2020 were analyzed and compared. Due to COVID-19 pandemic, there was a complete lockdown from 25th March to 31st May 2020 and phased reopening of areas outside containment zones from June 1st onwards. It is found that the average concentration of all the three pollutants at all four stations was reduced by about 50 % to 75 % due to lockdown. During lockdown, the minimum concentration of PM10, SO2 and NO2 amongst all stations were found to be 40, 5 and 11 µg/m3, respectively, whereas the maximum concentrations were found to be 159, 20, and 50 µg/m3, respectively. The concentrations during lockdown were below the standards prescribed by CPCB, which were found to increase due to reopening. The Air quality index (AQI) at all four stations during lockdown was less than 50 (i.e. SATISFACTORY), whereas it increased above 100 (i.e. MODERATE) after reopening. As a result, the annual average concentration of pollutants was reduced in 2020 compared to previous years.
TL;DR: The proposed framework creates a condensed immersive video using various optimization techniques by reducing collisions between objects, preserving all events with interaction, chronological ordering, and showing the viewer only the specified number of objects per frame in the synopsis video recognizing important actions.
TL;DR: In this paper , the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020 were analyzed and compared.
Abstract: The present paper deals with the studies on the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020. The monthly average concentrations of the above pollutants observed at four monitoring stations in and around Nagpur city during January to December 2020 were analyzed and compared. Due to COVID-19 pandemic, there was a complete lockdown from 25th March to 31st May 2020 and phased reopening of areas outside containment zones from June 1st onwards. It is found that the average concentration of all the three pollutants at all four stations was reduced by about 50 % to 75 % due to lockdown. During lockdown, the minimum concentration of PM10, SO2 and NO2 amongst all stations were found to be 40, 5 and 11 µg/m3, respectively, whereas the maximum concentrations were found to be 159, 20, and 50 µg/m3, respectively. The concentrations during lockdown were below the standards prescribed by CPCB, which were found to increase due to reopening. The Air quality index (AQI) at all four stations during lockdown was less than 50 (i.e. SATISFACTORY), whereas it increased above 100 (i.e. MODERATE) after reopening. As a result, the annual average concentration of pollutants was reduced in 2020 compared to previous years.