Novi gen algoritam za detekciju propada i poskoka napona na jednofaznom izmjenjivaču u mikromreži

In this manuscript, a novel sag and peak detector by means of a delta square operation for a single-phase is suggested. The established sag detector is from a single phase digital phase-locked loop (DPLL) that is founded on a d-q transformation employing an all-pass filter (APF). The d-q transformation is typically employed in the three-phase coordinate system. The APF produces a virtual phase with a 90 deg phase delay, but the virtual phase can not reproduce an abrupt variation of the grid voltage, at the moment in which the voltage sag transpires. As a consequence, the peak value is severely garbled, and settles down gradually. A modified APF produces the virtual q-axis voltage factor from the difference between the current and the former value of the d-axis voltage component in the stationary reference frame. Nevertheless, the amended APF cannot sense the voltage sag and peak value when the sag transpires around the zero crossing points such as 0 deg and 180 deg since the difference voltage is not adequate to sense the voltage sag. The suggested algorithm is proficient to sense the sag voltage through all regions as well as the zero crossing voltage. Furthermore, the precise voltage drop can be obtained by computing the q-axis component, which is relational to the d-axis component. To authenticate the legitimacy of the suggested scheme, the orthodox and suggested approaches are contrasted by means of the simulations and investigational results.U ovom radu je predložen novi detektor propada i poskoka napona korištenjem delta kvadratične operacije za jednu fazu. Predloženi detektor propada napona je u digitalnoj fazno-zatvorenoj petlji (DPLL) zasnovanoj na d-q transformaciji koja koristi svepropusni filtar (APF). D-q transformacija se tipično koristi u trofaznim koordinatnim sustavima. APF generira virtualnu fazu s 90 deg faznog kašnjenja, ali virtualna faza ne može reproducirati skokovitu promjenu napona mreže u trenutku u kojem se događa propad napona. Kao posljedica, detektirana vršna vrijednost se značajno izmijeni i smiruje se postepeno. Modificirani APF generira faktor napona virtualne q osi iz razlike između struje i prošle vrijednosti komponente napona na d osi u stacionarnom koordinatnom sustavu. Međutim, izmijenjeni APF ne može detektirati propad i poskok napona kada se propad događa u okolini točaka presijecanja nule, kao što je 0 deg i 180 deg s obzirom da diferencijski napon nije prikladan za detekciju propada napona. Predloženi algoritam je prilagođen detekciji propada napona u cijelom radnom području, uključujući i napon prelaska nule. Nadalje, precizni propad napona može se dobiti izračunom komponente napona na q osi, koja je u odnosu s obzirom na komponentu d osi. Za validaciju predloženih metoda provedena je njihova usporedba s konvencionalnim metodama u simulacijskom i eksperimentalnom okruženju

‎Provenance Based Trust Boosted Recommender System Using Boosted Vector Similarity Measure

‎As users in an online social network are overwhelmed by the abundant amount of information‎, ‎it is very hard to retrieve the preferred or required content‎. ‎In this context‎, ‎an online recommender system helps to filter and recommend content such as people,items or services‎. ‎But‎, ‎in a real scenario‎, ‎people rely more on recommendations‎ ‎from trusted sources than distrusting sources‎. ‎Though‎, ‎there are many trust based recommender systems that exist‎, ‎it lag in prediction error‎. ‎In order to improve the accuracy of the prediction‎, ‎this paper proposes a Trust-Boosted Recommender System (TBRS)‎. ‎Since‎, ‎the provenance derives the trust in a better way than other approaches‎, ‎TBRS is built‎ ‎from the provenance concept‎. ‎The proposed recommender system takes the provenance based fuzzy rules which were derived from the Fuzzy Decision Tree‎. ‎TBRS then computes the multi-attribute vector similarity score and boosts the score with trust weight‎. ‎This system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.The performance of the proposed method is evaluated in terms of MAE and RMSE‎. ‎The result shows that the error value of boosted similarity is lesser than without boost‎. ‎The reduced error rates of the Jaccard‎, ‎Dice and Cosine similarity measures are 18\%‎, ‎15\% and 7\% respectively‎. ‎Also‎, ‎when the model is subjected to failure analysis‎, ‎it gives better performance for unskewed data than slewed data‎. ‎The models fbest‎, ‎average and worst case predictions are 90\%‎, ‎50\% and $<$23\% respectively‎

SSNN-based energy management strategy in grid connected system for load scheduling and load sharing

The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems

A deep learning approach for recognizing the cursive Tamil characters in palm leaf manuscripts

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy

Generative adversarial networks for unmanned aerial vehicle object detection with fusion technology

Unmanned aerial vehicles (UAVs) also called as a drone comprises of a controller from the base station along with a communications system with the UAV. The UAV plane can be precisely controlled by a machine operator, similar to remotely directed aircraft, or with increasing grades of autonomy, as like autopilot assistance, up to completely self-directed aircraft that require no human input. Obstacle detection and avoidance is important for UAVs, particularly lightweight micro aerial vehicles, but it is a difficult problem to solve because pay load restrictions limit the number of sensors that can be mounted onto the vehicle. Lidar uses Laser for finding the distance between objects and vehicle. The speed and direction of the moving objects are detected and tracked with the help of radar. When many sensors are deployed, both thermal and electro-optro cameras have great clustering capabilities as well as accurate localization and ranging. The purpose of the proposed architecture is to create a fusion system that is cost-effective, lightweight, modular, and robust as well. Also, for tiny object detection, we recommend a novel Perceptual Generative Adversarial Network method that bridges the representation gap between small and large objects. It employs the Generative Adversarial Networks (GAN) algorithm, which iimproves object detection accuracy above benchmark models at the same time maintaining real-time efficiency in an embedded computer for UAVs. Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. At the same time, its discriminator contests with the generator to classify the engendered representation, imposing a perceptual restriction on the generator: created representations of tiny objects must be helpful for detection. With three different obstacles, we were able to successfully identify and determine the magnitude of the barriers in the first trial. The accuracy of proposed models is 83.65% and recall is 81% which is higher than the existing models

Sentiment analysis on COVID-19 Twitter data streams using deep belief neural networks

Social media is Internet-based by design, allowing people to share content quickly via electronic means. People can openly express their thoughts on social media sites such as Twitter, which can then be shared with other people. During the recent COVID-19 outbreak, public opinion analytics provided useful information for determining the best public health response. At the same time, the dissemination of misinformation, aided by social media and other digital platforms, has proven to be a greater threat to global public health than the virus itself, as the COVID-19 pandemic has shown. The public's feelings on social distancing can be discovered by analysing articulated messages from Twitter. The automated method of recognizing and classifying subjective information in text data is known as sentiment analysis. In this research work, we have proposed to use a combination of preprocessing approaches such as tokenization, filtering, stemming, and building N-gram models. Deep belief neural network (DBN) with pseudo labelling is used to classify the tweets. Top layers of the base classifiers are boosted in the pseudo labelling strategy, whereas lower levels of the base classifiers share weights for feature extraction. By introducing the pseudo boost mechanism, our suggested technique preserves the same time complexity as a DBN while achieving fast convergence to optimality. The pseudo labelling improves the performance of the classification. It extracts the keywords from the tweets with high precision. The results reveal that using the DBN classifier in conjunction with the bigram in the N-gram model outperformed other models by 90.3 percent. The proposed approach can also aid medical professionals and decision-makers in determining the best course of action for each location based on their views regarding the pandemic

Examining the effect of cyber twin and blockchain technologies for industrial applications using AI

In current generation the concept of cyber twin technology has been emerging as an improved platform for different applications. This paper emphasize on examining the effect of cyber twin technology for manufacturing equipment in Industry 4.0 applications by solving three different elementary objectives. For the proposed conception a new system model is identified for integrating triobjective cases with artificial intelligence algorithm. In addition, high security measures are also incorporated using blockchain technology which is one basic requirement for industrial applications for creating real twins. Both system model and algorithm have been combined for providing effective performance in real time using a physical entity. The effectiveness of the proposed model is tested with sensor prototype and simulated with four scenarios where the projected model provides better performance for more than 72% when compared with existing methodologies

A novel strategy for waste prediction using machine learning algorithm with IoT based intelligent waste management system

Internet of Things (IoT) has now become an embryonic technology to elevate the whole sphere into canny cities. Hasty enlargement of smart cities and industries leads to the proliferation of waste generation. Waste can be pigeon-holed as materials-based waste, hazard potential based waste, and origin-based waste. These waste categories must be coped thoroughly to make certain of the ecological finest run-throughs irrespective of the origin or hazard potential or content. Waste management should be incorporated into ecological preparation since it is a grave piece of natural cleanliness. The most important goalmouth of waste management is to maintain the pecuniary growth and snootier excellence of life by plummeting and exterminating adversative repercussions of waste materials on environment and human health. Disposing of unused things is a significant issue, and this ought to be done in the best manner by deflecting waste development and keeping hold of cost, and it involves countless human resources to deal with the waste. These current techniques predominantly focus on cost-effective monitoring of waste management, and results are not imprecise, so that it could not be developed in real time or practically applications such as in educational organizations, hospitals, and smart cities. Internet of things-based waste management system provides a real-time monitoring system for collecting the garbage waste, and it does not control the dispersion of overspill and blowout gases with poor odor. Consequently, it leads to the emission of radiation and toxic gases and affects the environment and social well-being and induces global warming. Motivated by these points, in this research work, we proposed an automatic method to achieve an effective and intelligent waste management system using Internet of things by predicting the possibility of waste things. The wastage capacity, gas level, and metal level can be monitored continuously using IoT based dustbins, which can be placed everywhere in city. Then, our proposed method can be tested by machine learning classification techniques such as linear regression, logistic regression, support vector machine, decision tree, and random forest algorithm. The proposed method is investigated with machine learning classification techniques in terms of accuracy and time analysis. Random forest algorithm gives the accuracy of 92.15% and time consumption of 0.2 milli seconds. From this analysis, our proposed method with random forest algorithm is significantly better compared to other classification techniques

Design and implementation of automatic water spraying system for solar photovoltaic module

Photovoltaic (PV) cell has a characteristic of decrease in power beyond a certain temperature. This decrease in power is due to a drop in the open circuit cell voltage. This decreases the efficiency of the PV cell. The objective of this research is to increase the efficiency of PV cells by reducing the PV cell temperature and reflection loss. The cell temperature and reflection loss can be reduced by spraying water over the PV cells. On spraying water over the USP36, 24V PV module, the power is found to be increased. The test result shows a 1V to 2V increase in voltage, with an efficiency increment of 1% to 1.27%. The test results of USP37 show the voltage increase of 1.2 V to 2.1 V in the PV module voltage. Due to the increase in voltage, efficiency increment of 1.29% is observed. The efficiency of USP36 with water spraying is more than the efficiency of USP37 without water spraying. In the PV power systems, an average increase in efficiency of 0.5% is observed

Power and area efficient cascaded effectless GDI approximate adder for accelerating multimedia applications using deep learning model

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder’s design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy