Deep Learning Stack LSTM Based MPPT Control of Dual Stage 100 kWp Grid-Tied Solar PV System

Rising global energy demand, predominantly satisfied by fossil fuels, triggers fuel price surges, fuel scarcity, and substantial greenhouse gas emissions. Solar photovoltaics (PV), as an abundant renewable alternative, can potentially address this demand, yet low cell efficiency (15-25%) and fluctuating output power due to intermittent irradiance (G) and temperature (T) impedes grid integration. This paper presents a novel Deep Learning (DL) based stacked LSTM (Long Short-Term Memory) MPPT controller to maximize power harvesting from a 100 kW grid-tied solar PV system, demonstrating superiority over conventional Perturb & Observe (P&O) and Feed Forward-Deep Neural Network (FF-DNN) MPPT approaches. Subsequently, a Neutral-Point-Clamped (NPC) 3-level inverter with proportional-integral (PI) controllers regulates the DC link voltage and transfers the extracted PV power to the grid. The proposed MPPT methodology includes collection of one million-sample (G, V, Vmp) datasets; preprocessing via z-score normalization; visualizing distributions through histograms and correlation matrix plots; an 80/20 split rule-based training and test sets; a two-hidden layer stacked LSTM (64 and 32 neurons) architecture; hyperparameters including the Adam optimizer, 0.05 learning rate, 32 batch size, and 50 epochs. Model efficacy quantification uses MSE, RMSE, MAE, loss, and R2 metrics. For 100 kW generated PV power, the stacked LSTM extracts 98.2 kW, versus 96.1 kW and 94.3 kW for the DNN and P&O MPPTs respectively. By integrating the optimized proposed stack LSTM MPPT with a streamlined inverter topology, the proposed approach advances the state-of-the-art in DL based solar PV energy harvesting optimization and grid integration

Countering the logic of the war economy in Syria

The country has entered a vicious circle where Syria’s own resources are being used to destroy it, and where ordinary people have no choice but to rearrange their lives around the conflict and either join or pay armed actors to meet everyday needs

Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach that goes beyond the use of quality flags and upper envelope filtering is tested to detect when and where long-duration clouds are responsible for unreliable NDVI readings, so that a user can flag those data as missing. The study is based on MODIS Terra and the combined Terra-Aqua 16-day NDVI product for the south of Ghana, where persistent cloud cover occurs throughout the year. The combined product could be assumed to have less cloud contamination, since it is based on two images per day. Short-duration cloud effects were removed from the two products through using the adaptive Savitzky–Golay filter. Then for each ‘cleaned’ product an unsupervised classified map was prepared using the ISODATA algorithm, and, by class, plots were prepared to depict changes over time of the means and the standard deviations in NDVI values. By comparing plots of similar classes, long-duration cloud contamination appeared to display a decline in mean NDVI below the lower limit 95% confidence interval with a coinciding increase in standard deviation above the upper limit 95% confidence interval. Regression analysis was carried out per NDVI class in two randomly selected groups in order to statistically test standard deviation values related to long-duration cloud contamination. A decline in seasonal NDVI values (growing season) were below the lower limit of 95% confidence interval as well as a concurrent increase in standard deviation values above the upper limit of the 95% confidence interval were noted in 34 NDVI classes. The regression analysis results showed that differences in NDVI class values between the Terra and the Terra-Aqua imagery were significantly correlated (p < 0.05) with the corresponding standard deviation values of the Terra imagery in case of all NDVI classes of two selected NDVI groups. The method successfully detects long-duration cloud contamination that results in unreliable NDVI values. The approach offers scientists interested in time series analysis a method of masking by area (class) the periods when pre-cleaned NDVI values remain affected by clouds. The approach requires no additional data for execution purposes but involves unsupervised classification of the imagery to carry out the evaluation of class-specific mean NDVI and standard deviation values over time

An Empirical Analysis of Inventory Turnover Performance Within a Local Chinese Supermarket

Retail inventory is an important indicator for retailers as well as their shareholders and suppliers. Inventory enables retailer to sell products to customer but excessive or slow moving inventory also add extra cost. For shareholders and suppliers this is an indication of retailer’s bright or grim future. The aim of this research is to analyze the inventory turnover’s impact on the performance variables of profit margin percentage and sale surprise in one of the retailing firm of Hubei province China. We will study if inventory turnover is affected by profit margin percentage and sale surprise similarly across all categories and modes of operation in retail firm or there is some variation in the known behavior. We will be testing our hypothesis on data of a large local supermarket chain that operates in the Hubei province of China. They have multiple supermarkets in the tier 1 and tier 2 cities of the province. We investigate correlation of inventory turnover with profit margin percentage and sale surprise across different categories and modes of operation. The analysis reveals that there is a negative correlation between Inventory Turnover and profit margin percentage, while positive correlation exists between Inventory Turnover and Sale surprise across all categories and modes. But its rate of correlation varies between categories and channel structure

The Effect of Product Variety on Inventory Turnover in Different Modes of Operation

We study the effects of product variety on operational metrics - inventory turnover and on sales in different mode of operations. Research has shown that performance metrics can vary in different mode of operations. Using 41 months of data from a large retailer, we show that correlation of product variety with inventory turnover and sales is not always negative or positive as shown in previous studies. This correlation can vary depending upon the mode of operations and type of product. Our study highlights impact of increased product variety on inventory turnover and sales in different mode of operations that has previously been overlooked in studies of retail product variety and inventory management. It also quantifies the impact of product variety on inventory turnover and sales

On the Resummed Hadronic Spectra of Inclusive B Decays

In this paper we investigate the hadronic mass spectra of inclusive B decays. Specifically, we study how an upper cut on the invariant mass spectrum, which is necessary to extract V_{ub}, results in the breakdown of the standard perturbative expansion due to the existence of large infrared logs. We first show how the decay rate factorizes at the level of the double differential distribution. Then, we present closed form expressions for the resummed cut rate for the inclusive decays B -> X_s gamma and B -> X_u e nu at next-to-leading order in the infrared logs. Using these results, we determine the range of cuts for which resummation is necessary, as well as the range for which the resummed expansion itself breaks down. We also use our results to extract the leading and next to leading infrared log contribution to the two loop differential rate. We find that for the phenomenologically interesting cut values, there is only a small region where the calculation is under control. Furthermore, the size of this region is sensitive to the parameter \bar{\Lambda}. We discuss the viability of extracting V_{ub} from the hadronic mass spectrum.Comment: 18 pages, 5 figures, minor change

Symmetric encryption relying on chaotic henon system for secure hardware-friendly wireless communication of implantable medical systems

Healthcare remote devices are recognized as a promising technology for treating health related issues. Among them are the wireless Implantable Medical Devices (IMDs): These electronic devices are manufactured to treat, monitor, support or replace defected vital organs while being implanted in the human body. Thus, they play a critical role in healing and even saving lives. Current IMDs research trends concentrate on their medical reliability. However, deploying wireless technology in such applications without considering security measures may offer adversaries an easy way to compromise them. With the aim to secure these devices, we explore a new scheme that creates symmetric encryption keys to encrypt the wireless communication portion. We will rely on chaotic systems to obtain a synchronized Pseudo-Random key. The latter will be generated separately in the system in such a way that avoids a wireless key exchange, thus protecting patients from the key theft. Once the key is defined, a simple encryption system that we propose in this paper will be used. We analyze the performance of this system from a cryptographic point of view to ensure that it offers a better safety and protection for patients. 2018 by the authors.Acknowledgments: This publication was made possible by NPRP grant #8-408-2-172 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Effect of mulching and organic manure on growth and yield performance of wheat

An experiment was conducted at the Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh in&nbsp;Rabi&nbsp;season (dry season) of 2014 to study the effect of mulching and organic manure on growth and yield performance of wheat. Five mulching practices viz. M1=1 irrigation at 17-21 days after sowing (DAS), M2=2 irrigations at 17-21 and 55-60 DAS, M3=3 irrigations at 17-21, 55-60 and 75-80 DAS, M4=control, M5=straw mulch (6 t ha-1) and five organic manure managements viz. O1=recommended chemical fertilizer (NPKS @ 100-23-20-16 kg ha-1), O2=poultry manure @ 6 t ha-1&nbsp;(100% PM), O3=vermicompost @ 8 t ha-1&nbsp;(100% VC), O4=50% chemical fertilizer+50% VC and O5=50% chemical fertilizer+50% PM were used as experimental variables. The experiment was conducted in split-plot design with three replications. The results showed that mulching had significant influence on all attributes. The highest values of all attributes were found in straw mulch treatment. It was observed that organic manure had significant influences on all characters. The highest values of yield and yield attributes were found in O5&nbsp;(50% chemical fertilizer+50% PM) treatment. It was observed that effective tillers hill-1, grain yield and straw yield were significantly affected by combined effect of mulching and organic manure. The highest values obtained from mulching and O5&nbsp;(50% chemical fertilizer+50% PM) treatment. Therefore, it can be inferred from the results of the study that highest production could be obtained from mulching and O5&nbsp;(50% chemical fertilizer+50% PM) treatment

Human Detection Framework for Automated Surveillance Systems

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system