Vertical axis wind turbine application for power generation

Concerns over security for energy sources have led many countries to concentrate on obtaining renewable energy sources. In fact, Malaysia has also studied various alternative energy sources including wind energy. However, the terrain of Malaysia does not allow strong winds to move the wind turbines. Therefore, initial efforts to harness energy from the wind were not very successful. Therefore, this study has improved previous studies by highlighting the concept of vertical axis wind turbine using Magnus effect concept

Radiation pattern performance of bow tie patch antenna for ground penetrating radar (GPR) applications

This paper presents a directional bow tie patch antenna for ground penetrating radar (GPR) applications. There are four proposed designs for this paper. Three of the antenna is designed by introducing Sierpinski gasket fractal concept on bow tie patch antenna. There is some modification on this design in order to create a new different fractal design that is applicable for GPR applications. The bow tie antenna performance is studied across 1 GHz to 4 GHz. The best return loss obtained for this paper is at 3.7 GHz where all four designs have its best performance. The comparison at 3.7 GHz of these four antenna designs presented in this pape

DEMYSTIFYING SHIP OPERATIONAL AVAILABILITY – AN ALTERNATIVE APPROACH FOR THE MAINTENANCE OF NAVAL VESSELS

Asset availability improvement has been the focus of many studies by various industries for a few decades now, and the defence industry is no exception. To date, there exists no simple and inexpensive high availability solution for the complex naval ships consisting of many interdependent systems and subsystems working in parallel. Any given approach must strike a balance between true needs and economics, an ever-increasing decision-making burden to stakeholders. Nevertheless, there are many ways to approach the problem. In the past, availability has been viewed as complex mathematical calculations and estimates involving defective equipment. The applied approach has not been fully understood nor appealing to most practitioners as well as the majority of stakeholders who continuously complain about the gap between theory and practice. This paper aims to demystify the complex naval ship availability issue, simplified for easy understanding of operators, maintainers and logisticians as well as other stakeholders involved in the maintenance of naval vessels. The stepby-step approach begins with the identification of severe factors involving both human and machinery affecting downtime of naval vessels culminating into the generation of an availability-oriented model, summarized to a simple four-step approach to availability improvement. Practitioners are now able to appreciate their individual contribution towards improving ship availability

Reliability Monitoring of GNSS Aided Positioning for Land Vehicle Applications in Urban Environments

Cette thèse porte sur les défis en matière de surveillance de la fiabilité de la navigation par GNSS pour les applications de véhicules terrestres dans les milieux urbains. L'objectif principal de cette recherche est de développer des méthodes de positionnement avec confiance en utilisant des mesures GNSS et des mesures de confiance pour l'utilisateur dans des environnements urbains contraintes. Dans la première partie de la thèse, les erreurs NLOS en milieu urbain sont caractérisées par un modèle 3D de l'environnement urbain. Dans la deuxième partie de la thèse, nous avons proposé une technique de surveillance de la fiabilité dans le domaine des mesures GNSS pour l'environnement urbain en utilisant un capteur de vitesse fiable. Enfin, nous avons développé une nouvelle expérimentale de surveillance de l'intégrité pour le positionnement en milieu urbain. En surveillant de la statistique de test contre un seuil spécifique, l'intégrité et la continuité de positionnement sont fixés à un certain niveau de confiance. En outre, le calcul de niveau de protection horizontale (HPL) en utilisant une approche composite a également été proposé.This thesis addresses the challenges in reliability monitoring of GNSS aided navigation for land vehicle applications in urban environments. The main objective of this research is to develop methods of trusted positioning using GNSS measurements and confidence measures for the user in constrained urban environments. In the first part of the thesis, the NLOS errors in urban settings are characterized by means of a 3D model of the urban surrounding. For the second part of the thesis, the work proposes a reliability monitoring technique in the range domain for urban environ ment using a trusted velocity sensor. Finally, the research developed a novel experimental scheme in integrity monitoring for positioning in urban environment. By monitoring the test statistic against a specific threshold, the positioning integrity and continuity are met at a certain level of confidence. In addition, the Horizontal Protection Level (HPL) computation using a composite approach has also been proposed

Characterization of GNSS Receiver Position Errors for User Integrity Monitoring in Urban Environments

International audienceThe characterization of GNSS position errors in urban environments is an important issue for integrity monitoring and classification of receivers’ performance. However, these errors are not observable directly by the receiver, therefore RAIM methods use statistics based on the pseudorange residuals (i.e. observable errors). In this work, we focus on the modelling and analysis of navigation errors in the position-domain rather than individual range-domain errors that are difficult to model in urban environments due to multipath and non-line-of-sight (NLOS) signals. Using a trajectory of reference we compute the horizontal position errors (HPE) and its non-parametric distribution function given by the empirical Cumulative Distribution Function (CDF). According to the results, we observe that these errors have a heavy-tailed distribution, and then we propose to fit the empirical CDF with the CDF of the generalized Pareto distribution (GPD). We use an inflated version of the fitted Pareto model to overbound the CDF of the HPE for the calculation of Horizontal Protection Level (HPL), i.e., bounding the radial position errors

A secure and dependable trust assessment (SDTS) scheme for industrial communication networks

Abstract Due to tamper-resistant sensor nodes and wireless media, Industrial Wireless Sensor Networks (WSNs) are susceptible to various security threats that severely affect industrial/business applications. The survival of sensor networks is highly dependent on the flourishing collaboration of sensor nodes. Trust management schemes seem to be realistic and promising techniques to improve security as well as cooperation (dependability) among sensor nodes by estimating the trust level (score) of individual sensor nodes. This research paper presents a well-organized and motivating secure, dependable trust assessment (SDTS) scheme for industrial WSNs to cope with unexpected behavior such as an on–off attack, bad-mouthing attack, garnished attack, etc., by employing robust trust evaluation components based on success ratio and node misbehaviour. SDTS incorporates an interesting trust evaluation function in which the trust range can be adjusted in accordance with the application requirement. SDTS include direct communication trust, indirect communication trust, data trust, and misbehavior-based trust to defend the multiple internal attacks. SDTS works according to the behavior of nodes, i.e., whether the sensor nodes are interacting frequently or not. Moreover, abnormal attenuation and dynamic slide lengths are incorporated in the proposed model (SDTS) to deal with various natural calamities and internal attacks. SDTS is compared against three recent state-of-the-art methods and found efficient in terms of ease of trust assessment, false-positive rate (2.5%), false-negative rate (2%), attack detection rate (90%), detection accuracy (91%), average energy consumption (0.40 J), and throughput (108 Kbps) under the load of 500 sensor nodes with 50% malicious nodes. Investigational results exhibit the potency of the proposed scheme

Performance comparison between NoSQL (RethinkDB) and MySQL database replication from master to slave in big data

When database is stored in one computer or server, certain issues related to disaster (in terms of natural or un-natural) problem may arise with reference to geographical connection and geographical distribution of servers. Here, replication feature plays an important role. There were many techniques and methodologies for geographically distributed servers, but the problem was of one master. When an authorized user updates his database on a client database server then this client database server updates his master database server and at last, the master database server updates all its client’s database servers. The described process is called database replication. In this process, there are several main parameters that are important, i.e., bandwidth, memory, and processor time. In this paper, the analysis is focused on comparing which database (NoSQL family-RethinkDB and MySQL) utilize how much memory, processor, and time during replication of database from client to master

Employing an Energy Harvesting Strategy to Enhance the Performance of a Wireless Emergency Network

Establishing a wireless communication network (WCN) is critical to saving people’s lives during disasters. Since the user equipment (UE) must transfer their information to the functioning area, their batteries will be significantly drained. Thus, technologies that can compensate for battery power consumption, such as the energy harvesting (EH) strategy, are highly required. This paper proposes a framework that employs EH at the main cluster head (MCH) selected by the enhanced clustering technique (CFT) and simultaneously transmits information and power wirelessly to prolong the lifetime of the energy-constrained network. MCH harvests energy from the radio frequency signal via the relay station (RS) and uses the harvested energy for D2D communications. The suggested framework was evaluated by analyzing the EH outage probability and estimating the energy efficiency performance, which is expected to improve the stability of the network. Compared to the UAV scenario, the simulation findings show that when RS is in its optimal location, it enhances the network EH outage probability performance by 26.3%. Finally, integrating CFT with wireless communications links into cellular networks is an effective technique for maintaining communication services for mission-critical applications

PAPQ: Predictive analytics of product quality in industry 4.0

In e-commerce, Industry 4.0 is all about combining analytics, artificial intelligence, and machine learning to simplify procedures and enable product quality review. In addition, the importance of anticipating client behavior in the context of e-commerce is growing as individuals migrate from visiting physical businesses to shopping online. By providing a more personalized purchasing experience, it can increase consumer satisfaction and sales, leading to improved conversion rates and competitive advantage. Using data from e-commerce platforms such as Flipkart and Amazon, it is possible to build models for forecasting customer behavior. This study examines machine learning techniques for product quality prediction and gives an insight into the performance differences of machine learning-based models by doing descriptive data analysis and training each model separately on three datasets viz Mobile, Health Equipments, and Book Datasets. Support Vector Machine, Nave Bayes, k-Nearest Neighbors, Random Forest, and Random Tree were the machine learning methods utilized in this work. The results indicate that a Support Vector Machine Model provides the greatest fit for the prediction task, with the best performance, reasonable latency, comprehensibility, and resilience for the first two datasets, but Random Forest provides the highest performance for the Book dataset