Microgrid management using hybrid inverter fuzzy-based control

Neural Computing and Applications - Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly...     

DLSA: Delay and Link Stability Aware Routing Protocol for Flying Ad-hoc Networks (FANETs)

Flying Ad-hoc Network (FANET) is a new class of Mobile Ad-hoc Network in which the nodes move in three-dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by the pre-defined mechanism which involves no human personnel. Due to the high mobility of nodes and dynamic topology, link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with the highest threshold value by maximizing Packet Delivery Ratio and minimizing End-to-End Delay. In this paper, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing and Link Stability Estimation-based Routing FANET’s existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features of collaborative data forwarding and link stability. The simulation results have shown the improved performance of the proposed DLSA routing protocol in contrast to the selected existing ones DPTR and LEPR in terms of E2ED, PDR, Network Lifetime, and Transmission Loss. The Average E2ED in milliseconds of DLSA was measured 0.457 while DPTR was 1.492 and LEPR was 1.006. Similarly, the Average PDR in %age of DLSA measured 3.106 while DPTR was 2.303 and LEPR was 0.682. The average Network Lifetime of DLSA measured 62.141 while DPTR was 23.026 and LEPR was 27.298. At finally, the Average Transmission Loss in dBm of DLSA measured 0.975 while DPTR was 1.053 and LEPR was 1.227.

     

OpenSim: open-source software to create and analyze dynamic simulations of movement

Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.     

Time-based DDoS attack detection through hybrid LSTM-CNN model architectures: An investigation of many-to-one and many-to-many approaches

Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto-feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co-learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta-learning. The models are configured in both many to one and many to many design architectures. We combine long short-term memory (LSTM), bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false-positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well-known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.     

A qualitative investigation on the importance of boundary layer in pervaporation separation of an aqueous organic reaction

The importance of boundary layer was investigated for the hydrolysis of ethyl acetate using a dense standard poly(vinyl alcohol) (PVA) membrane. A resistance in a series model approach was used to study the effect of a boundary layer for the permeation of aqueous organic mixtures involved in the study. The initial feed mixture consisted of 10% water (H₂O), 40% ethyl acetate (EA), 50% acetic acid (AA), and 0% ethanol (E) (all weight percentage basis). The experiments were conducted at 65°C. The amount of all species in the feed reservoir was found to decrease with time. Selectivity calculations based on a resistance in the series model approach indicates that the boundary layer contributes to selective permeation of aqueous organic compounds. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 70: 2361–2369, 1998     

Synthesis and evaluation of some novel additives as antioxidants and corrosion inhibitors for petroleum fractions

Benzoxazinone 2 was prepared and reacted with formamide,acetamide,some primary aromatic amines and heterocyclic amines giving the corresponding quinazolone derivatives 3-15 respectively.The reaction of benzoxazinone 2 with hydrazine hydrate and phenyl hydrazine was also studied.Representative compounds of the synthesized products were evaluated as antioxidants and corrosion inhibitors for gasoline engine lubricating oil.The highest antioxidant activities were obtained with compounds 10-15.The optimum concentration recommended for these new additives was found to be 0.lg for 1L ofoil for compounds 13-15.In addition,some of the highly effective antioxidant additives,namely 10-15,were thermally analyzed by using thermogravimetric analysis （TGA） and differential thermal gravimetric analysis （DTGA） techniques and the results indicated that compounds are thermally stable and could be used under thermal conditions.Moreover,a comparison of the oxidation stability between the tested oil containing the prepared products and lubricating oil containing commercial additives was also studied.     

One‐Pot Thioetherification of Aryl Halides Using Thiourea and Alkyl Bromides Catalyzed by Copper(I) Iodide Free from Foul‐Smelling Thiols in Wet Polyethylene Glycol (PEG 200)

In this article, we have developed a new protocol for the thioarylation of structurally diverse alkyl bromides such as benzyl, cinnamyl, n‐octyl, cyclohexyl, cyclopentyl, and tert‐butyl bromides with aryl iodides, bromides and an activated chloride using thiourea catalyzed by copper(I) iodide in wet polyethylene glycol (PEG 200) as an eco‐friendly medium in the presence of potassium carbonate at 80 and 100 °C under an inert atmosphere. The process is free from foul‐smelling thiols which makes this method more practical for the thioetherification of aryl halides. Another important feature of this method is the variety of alkyl bromides which are commercially available for the in situ generation of thiolate ions with respect to the existing protocols in which the less commercially available thiols are directly used for the preparation of arylthio ethers.     

Monitoring of Overhead Transmission Lines: A Review from the Perspective of Contactless Technologies

This paper describes a comprehensive review of non-contact technologies for overhead power transmission lines. Due to ever increasing emphasis on reducing accidents and speeding up diagnosis for automatically controlled grids, real time remote sensing and actuation is the new horizon for smart grid implementation. The technology overview with emphasis on the practical implementation of advanced non-contact technologies is discussed in this paper while considering optimization of the high voltage transmission lines parameters. In case of fault, the voltage and the current exceed limits of operation and hence real time reporting for control and diagnosis is a critical requirement. This paper aims to form a strong foundation for control and diagnosis of future power distribution systems so that a practitioner or researcher can make choices for a workable solution in smart grid implementation based on non-contact sensing.     

Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm.