A multi-terminal HVdc grid topology proposal for offshore wind farms

© 2020 by the authors. Although various topologies of multi-terminal high voltage direct current (MT-HVdc) transmission systems are available in the literature, most of them are prone to loss of flexibility, reliability, stability, and redundancy in the events of grid contingencies. In this research, two new wind farms and substation ring topology (2WF-SSRT) are designed and proposed to address the aforementioned shortcomings. The objective of this paper is to investigate MT-HVdc grid topologies for integrating large offshore wind farms with an emphasis on power loss in the event of a dc grid fault or mainland alternating current (ac)grid abnormality. Standards and control of voltage source converter (VSC) based MT-HVdc grids are defined and discussed. High voltage dc switch-gear and dc circuit topologies are appraised based on the necessity of dc cables, HVdc circuit breakers, and extra offshore platforms. In this paper, the proposed topology is analyzed and compared with the formers for number and ratings of offshore substations, dc breakers, ultra-fast mechanical actuators, dc circuits, cost, flexibility, utilization, and redundancy of HVdc links. Coordinated operation of various topologies is assessed and compared with respect to the designed control scheme via a developed EMTDC/PSCAD simulation platform considering three fault scenarios: dc fault on transmission link connecting the wind farm to mainland power converters, dc fault within substation ring of VSC-HVdc stations, and ultimate disconnection of grid side VSC station. Results show that 2WF-SSRT is a promising topology for future MT-HVdc grids

Do New Mobile Devices in Enterprises Pose A Serious Security Threat?

Abstract The purpose of this paper is to introduce a research proposal designed to explore the network security issues concerning mobile devices protection

Learning Outcomes of Educational Usage of Social Media: The Moderating Roles of Task–Technology Fit and Perceived Risk

This study aims to explore the moderating roles of task–technology fit (TTF) and perceived risk (PR) in the relationships between the educational usage of social media (SM) platforms and its use outcomes. This is to better understand the potential benefits of using SM for educational purposes and to provide thorough insights on how SM usage would influence students’ use outcomes. We conceptualize the potential use outcomes through three-dimensional factors: perceived satisfaction, perceived academic performance, and perceived impact on learning. We further hypothesize that TTF and PR have negative moderation effects on the relationships between SM usage and the variables of use outcomes. In addition, we examine gender differences using multi-group analysis. Data were collected from a state college in Palestine using a self-administered survey, and Smart-PLS was used for data analysis and model testing using partial least square–structural equation modeling. The findings reveal that TTF has significant negative effects on the relationships between SM usage and its outcomes, whereas PR has insignificant negative moderation effects. Despite the significant negative interaction effects of TTF, the educational usage of SM has a positive impact on use outcomes. Furthermore, the findings only indicate significant gender differences in three variables: information sharing, TTF, and PR

Error Analysis of Free Space Communication System Using Machine Learning

Free space optical (FSO) communication offers huge bandwidth, license-free spectrum and a more secure channel. PIN diodes are normally used for detection, but avalanche photodiodes (APD) are preferred for detecting high-speed FSO signals in many applications. In the case of APD, the noise distribution is input-dependent Gaussian noise (IDGN) rather than input-independent Gaussian noise (IIGN). We investigate the error analysis using on-off keying (OOK) for various detection approaches. This paper proposes a machine learning approach and compares its performance with soft and hard decisions. Soft values in the case of IDGN and IIGN are derived, and the optimum and sub-optimum detection thresholds are evaluated. The proposed novel ML approach shows better performance gains than the other approaches. It is also demonstrated that the IDGN model should have an optimum detection and achieve a gain of 2.5[dB] and about 1[dB] at $\lambda$ = 0[dB] and $\lambda$ = 10[dB], respectively. Experimental results are plotted for the FSO channel data, and a model fit curve is plotted using the ML approach

Design, Development, and Experimental Verification of a Trajectory Algorithm of a Telepresence Robot

Background: Over the last few decades, telepresence robots (TRs) have drawn significant attention in academic and healthcare systems due to their enormous benefits, including safety improvement, remote access and economics, reduced traffic congestion, and greater mobility. COVID-19 and advancements in the military play a vital role in developing TRs. Since then, research on the advancement of robots has been attracting much attention. Methods: In critical areas, the placement and movement of humans are not safe, and researchers have started looking at the development of robots. Robot development includes many parameters to be analyzed, and trajectory planning and optimization are among them. The main objective of this study is to present a trajectory control and optimization algorithm for a cognitive architecture named auto-MERLIN. Optimization algorithms are developed for trajectory control. Results: The derived work empirically tests the solutions and provides execution details for creating the trajectory design. We develop the trajectory algorithm for the clockwise direction and another one for the clockwise and counterclockwise directions. Conclusions: Experimental results are drawn to support the proposed algorithm. Self-localization, self-driving, and right and left turn trajectories are drawn. All of the experimental results show that the designed TR works properly, with better accuracy and only a slight jitter in the orientation. The jitter is found due to the environmental factor caught by the sensors, which can be filtered easily. The results show that the proposed approach is less complex and provides better trajectory planning accuracy

Design of a Telepresence Robot to Avoid Obstacles in IoT-Enabled Sustainable Healthcare Systems

In the Internet of Things (IoT) era, telepresence robots (TRs) are increasingly a part of healthcare, academia, and industry due to their enormous benefits. IoT provides a sensor-based environment in which robots receive more precise information about their surroundings. The researchers work day and night to reduce cost, duration, and complexity in all application areas. It provides tremendous benefits, such as sustainability, welfare improvement, cost-effectiveness, user-friendliness, and adaptability. However, it faces many challenges in making critical decisions during motion, which requires a long training period and intelligent motion planning. These include obstacle avoidance during movement, intelligent control in hazardous situations, and ensuring the right measurements. Following up on these issues requires a sophisticated control design and a secure communication link. This paper proposes a control design to normalize the integration process and offer an auto-MERLIN robot with cognitive and sustainable architecture. A control design is proposed through system identification and modeling of the robot. The robot control design was evaluated, and a prototype was prepared for testing in a hazardous environment. The robot was tested by considering various parameters: driving straight ahead, turning right, self-localizing, and receiving commands from a remote location. The maneuverability, controllability, and stability results show that the proposed design is well-developed and cost-efficient, with a fast response time. The experimental results show that the proposed method significantly minimizes the obstacle collisions. The results confirm the employability and sustainability of the proposed design and demonstrate auto-MERLIN’s capabilities as a sustainable robot ready to be deployed in highly interactive scenarios

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications