Enhancement of Power Quality in Domestic Loads Using Harmonic Filters

This study deals with the mitigation of current harmonics, which is primarily important to alleviate power quality problems in modern times. Current harmonics produced by different widely used loads have been evaluated and related parameters have been tabled. Using the data obtained, a non-linear load was modelled to serve as the test load. Different mitigation solutions and techniques were studied to select an appropriate filter design for domestic single-phase application. The Active Power Filter (APF)'s steady-state and dynamic output was evaluated with reference current extraction techniques like PQ and SRF theories in Simulink. For a fair comparison, various parameters related to the filter design were kept identical between the tests conducted; and to test the dynamic performance, a highly inductive load was connected halfway through simulation. The reactive power compensation offered by the filter was studied by using various waveforms and parameters are investigated and tabulated. The study was carried out to identify a reference current extraction technique that yields the best performance and understand the implementation of the same to identify inherent issues that can sometimes be overlooked because of their simplicity and ease of implementation. The performance of two commonly used reference current extraction techniques were analyzed by subjecting it to highly non-linear and highly inductive loads that were modelled based on various loads that were analyzed

Improving performance of multi-agent cooperation using epistemic planning

© 2017 Abeer Dhafer G. AlshehriIn multi-agent systems, a communication process is essential among agents to interact and coordinate their actions, and thus achieve their goal. However, communication has a related cost which affects the overall system performance. In this thesis, we draw inspiration from studies of the epistemic planning framework to develop a communication model for agents that allows them to cooperate and make communication decisions effectively within a multi-agent planning task. Our approach aims to develop a compact model that involves a communication action to be a part of planning task as a natural action. In simulated tasks motivated by a disaster scenario, we aim to investigate whether agents can cooperate effectively and achieve higher performance using a proposed communication model (a selective model)

Assessment of water contamination by potentially toxic elements in mangrove lagoons of the Red Sea, Saudi Arabia

Mangrove (Avicennia marina) forests in the Red Sea cost have great concern from environmental, biological, economic, and social points of view. Therefore, assessing water contamination in this ecosystem is worth to be investigated. Consequently, here we aimed to examine the levels of salinity, acidity, and the total content of Fe, Mn, Cu, Zn, Cd, Cr, Ni, and Pb in water samples collected from the upper, middle, and lower part of three mangrove lagoons (i.e., Al-Shuaiba, Yanbu, and Jeddah), Red Sea, Saudi Arabia. The total metal content (µg L−1) in water samples differed significantly among the studied areas and ranged from 286.2 to 4815.0 for Fe, 86.4–483.0 for Mn, 22.9–468.8 for Cu, 199.2–366.6 for Zn, 44.1–99.8 for Cd, 25.6–80.3 for Cr, 11.6–41.5 for Ni, and from 17.7 to 102.0 for Pb. The mean values of Cu, Zn, Cd, and Pb were higher than the WHO water quality standards for fisheries. Water samples in Yanbu were more contaminated and contained higher concentrations of all metals than Jeddah and Al-Shuaiba, due to the petrochemical industries in this industrial area. Our findings suggest that the high metal content in the water of these mangrove sites, particularly in Yanbu, should be considered due to the high potential environmental and human health risks in these ecosystems. These results may help for demonstrating effective approaches for the management of these lagoons. More studies will be carried out on the sediment and mangrove plants in this ecosystem.Bergische Universität Wuppertal (3089

Bi-Directional Adaptive Probabilistic Method With a Triangular Segmented Interpolation for Robot Path Planning in Complex Dynamic-Environments

Path planning is a fundamental aspect of mobile robots and autonomous systems. Methods of path planning are used in robotics to create a path for a robot or autonomous system to follow from a starting position to a goal one while avoiding obstacles and satisfying any additional conditions. There are many different methods to plan the path, including probabilistic methods, heuristics-based approaches, and optimization-based methods. In this paper, we introduce a novel path planning method called Dynamic Adaptive RRT-connect with Triangular Segmented Interpolation. Our approach aims to enhance the conventional Rapidly-exploring Random Tree (RRT) algorithms by incorporating an Adaptive-RRT strategy. This strategy involves selecting a random node as a new node to augment the exploration of the tree, thereby improving its coverage of the search space. Furthermore, we employ a Bi-directional scheme to further enhance the convergence time and cost of our method. By exploring the search space from both the initial and goal configurations simultaneously, we exploit the advantages of a two-way search, potentially resulting in more efficient and optimized paths. To improve the quality of the generated paths, our method leverages the Triangular Segmented Interpolation (TSI) technique. TSI helps in reducing the path length and increasing its smoothness by interpolating between the configurations in a triangular segmented manner, resulting in more natural and feasible trajectories. Moreover, considering the dynamic nature of the environment, our method operates within the framework of the Dynamic Window Approach (DWA). By adapting to the changing environment, our approach effectively avoids dynamic obstacles and navigates the robot or system through complex and unpredictable scenarios. We have conducted extensive experiments in various environments to evaluate the performance of our proposed method. The results demonstrate that our approach outperforms the individual RRT and RRT-connect algorithms in terms of computation time (reduced by 90-80%), cost (reduced by 82-63%), and path length (shortened by 17-12%). Additionally, our method exhibits efficient obstacle avoidance capabilities, enabling successful navigation in dynamic environments

Assessment of water contamination by potentially toxic elements in mangrove lagoons of the Red Sea, Saudi Arabia

Mangrove (Avicennia marina) forests in the Red Sea cost have great concern from environmental, biological, economic, and social points of view. Therefore, assessing water contamination in this ecosystem is worth to be investigated. Consequently, here we aimed to examine the levels of salinity, acidity, and the total content of Fe, Mn, Cu, Zn, Cd, Cr, Ni, and Pb in water samples collected from the upper, middle, and lower part of three mangrove lagoons (i.e., Al-Shuaiba, Yanbu, and Jeddah), Red Sea, Saudi Arabia. The total metal content (µg L⁻¹) in water samples differed significantly among the studied areas and ranged from 286.2 to 4815.0 for Fe, 86.4–483.0 for Mn, 22.9–468.8 for Cu, 199.2–366.6 for Zn, 44.1–99.8 for Cd, 25.6–80.3 for Cr, 11.6–41.5 for Ni, and from 17.7 to 102.0 for Pb. The mean values of Cu, Zn, Cd, and Pb were higher than the WHO water quality standards for fisheries. Water samples in Yanbu were more contaminated and contained higher concentrations of all metals than Jeddah and Al-Shuaiba, due to the petrochemical industries in this industrial area. Our findings suggest that the high metal content in the water of these mangrove sites, particularly in Yanbu, should be considered due to the high potential environmental and human health risks in these ecosystems. These results may help for demonstrating effective approaches for the management of these lagoons. More studies will be carried out on the sediment and mangrove plants in this ecosystem

Employee Attrition Prediction Using Deep Neural Networks

Decision-making plays an essential role in the management and may represent the most important component in the planning process. Employee attrition is considered a well-known problem that needs the right decisions from the administration to preserve high qualified employees. Interestingly, artificial intelligence is utilized extensively as an efficient tool for predicting such a problem. The proposed work utilizes the deep learning technique along with some preprocessing steps to improve the prediction of employee attrition. Several factors lead to employee attrition. Such factors are analyzed to reveal their intercorrelation and to demonstrate the dominant ones. Our work was tested using the imbalanced dataset of IBM analytics, which contains 35 features for 1470 employees. To get realistic results, we derived a balanced version from the original one. Finally, cross-validation is implemented to evaluate our work precisely. Extensive experiments have been conducted to show the practical value of our work. The prediction accuracy using the original dataset is about 91%, whereas it is about 94% using a synthetic dataset

Steady nanofluid flow between parallel plates considering thermophoresis and Brownian effects

In this article, heat and mass transfer behavior of steady nanofluid flow between parallel plates in the presence of uniform magnetic field is studied. The important effect of Brownian motion and thermophoresis has been included in the model of nanofluid. The governing equations are solved via the Differential Transformation Method. The validity of this method was verified by comparison of previous work which is done for viscous fluid. The analysis is carried out for different parameters namely: viscosity parameter, Magnetic parameter, thermophoretic parameter and Brownian parameter. Results reveal that skin friction coefficient enhances with rise of viscosity and Magnetic parameters. Also it can be found that Nusselt number augments with an increase of viscosity parameters but it decreases with augment of Magnetic parameter, thermophoretic parameter and Brownian parameter

Energy Efficient Dynamic Symmetric Key Based Protocol for Secure Traffic Exchanges in Smart Homes

Highly sensitive information about people’s social life and daily activities flows in smart home networks. As such, if attackers can manage to capture or even eavesdrop on this information, the privacy of the users can be compromised. The consequences can be far-reaching, such as knowing the status of home occupancy that can then facilitate burglary. To address these challenges, approaches such as data aggregation and signcryption have been utilized. Elliptic curve cryptography, bilinear pairing, asymmetric key cryptosystem, blockchain, and exponential operations are among the most popular techniques deployed to design these security solutions. However, the computational, storage and communication complexities exhibited by the majority of these techniques are too high. This renders these techniques unsuitable for smart home components such as smart switches and sensors. Some of these schemes have centralized architectures, which present some single points of failure. In this paper, symmetric key authentication procedures are presented for smart home networks. The proposed protocol leverages on cryptographic primitives such as one-way hashing and bitwise exclusive-Or operations. The results indicate that this scheme incurs the lowest communication, storage, and computation costs compared to other related state-of-the-art techniques. Empirically, our protocol reduces the communication and computation complexities by 16.7% and 57.7%, respectively. In addition, it provides backward key secrecy, robust mutual authentication, anonymity, forward key secrecy, and unlinkability. Moreover, it can effectively prevent attacks such as impersonation, session hijacking, denial of service, packet replays, man-in-the-middle, and message eavesdropping