Switched Reluctance Motor for Electric Submersible Pump

Switched reluctance motors may be advantageous when used as the primary motor for an electric submersible pump system. They are less susceptible to jamming failures due to their high starting torque and ability to reverse direction. Driving these motors requires well-timed pulse waveforms and precise control of the motor based on its rotational position. In general, voltage-based sensing and control systems at the surface see highly unpredictable waveforms with excessive ringing behaviour due to the impedance characteristics of the long cabling between the surface controller and the downhole motor system. In this work, a system is detailed which monitors the current waveforms on the motor coil excitation conductors at the surface as a source of motor performance feedback and control. State-space modelling of the system shows stable current waveforms at the surface controller for both short and long interconnect cable systems. A laboratory demonstration of the surface controller, interconnect cabling, and motor system is shows excellent agreement with the current and voltage waveforms predicted by the state-space system model.

Coupling Independent Operation in Wireless Power Transfer System without Ferrite Usage

The power transfer efficiency and output power of a wireless power transfer (WPT) system are mainly affected by magnetic coupling between the primary and secondary coils. This paper presents a constant-current series-series compensated WPT system. Based on the bifurcation criteria, kcri and Lcri, the splitting zero phase angle (ZPA) frequencies is adopted as the operating frequency. The proposed system remains fully compensated even under coupling variations, and without ferrite. The current and voltage gains at the operating frequency can be estimated through the primary current and voltage. A phase-locked loop circuit is used to track the corresponding ZPA frequency due to the coil positioning variations. Experimental results have shown that the 1-kW of output power with the satisfied efficiency of 96%.

The Effect of Hyperparameter Selection on the Personification of Customer Population Data

We explore the effects of hyperparameter selections on the personification accuracy of customer analytics data from a corporate YouTube channel with an audience in the hundreds of thousands and customer interactions in the tens of millions. Using non-negative matrix factorization, we generate personas sets from 5 to 15 using the customer analytics data, with the number of personas being the changing hyperparameter. We then compare the gender, age, nationality, and topical interests of the personas across each of the 11 persona sets using the average of the 110 generated personas as the baseline. This analysis shows that hyperparameter selection significantly alters the personification of the analytics data, with the effect most apparent with age representation. The set of 10 personas provides one of the most accurate representations across all attributes, indicating that this may be a good default hyperparameter for personification. Future research can explore other personification attributes with other customer analytics datasets.

Variable DC Approach in Fuel cell And Battery Powered Marine Power Systems: Control of The Fuel Cell Power Converter

Previously, the Variable DC approach concept was proposed for operation of hybrid fuel cell and battery powered marine vessels. The concept was shown to provide significant efficiency improvement, and consequently improved hydrogen fuel savings. However, although the general concept and the control of battery DC/DC converter has been detailly described in previous works, the operation of fuel cell DC/DC converter in different Variable DC approach control modes has not been presented. This paper proposes a fuel cell DC/DC converter control system specifically designed for operation in the three Variable DC approach control modes. The functionality of the proposed fuel cell DC/DC converter and the Variable DC approach in general is verified using a hardware-in-loop test setup consisting of virtual power stage models and real converter controllers. The system is shown to function well in both normal operating conditions and various fault conditions.

An optimal energy aware aperiodic task server for autonomous IoT sensors

A real-time applicative software consists of both aperiodic and periodic tasks. The periodic tasks have regular arrival times and strict deadlines. The aperiodic tasks have irregular arrival times and no deadline. The objective of an optimal aperiodic task server is to guarantee minimal response times for the aperiodic tasks and no violation of hard deadlines for periodic tasks. We consider a real-time energy harvesting system composed of energy harvester, energy storage unit, uniprocessing unit and the real-time tasks. We introduce a novel periodic task scheduler, namely SSP which is an extension of the slack stealing server so as to cope with fluctuations in energy availability. Experimental results show that the proposed algorithm achieves better performance in terms of aperiodic responsiveness. Simulations have been conducted for various settings of workloads and harvested energy profiles.

A Study on the Effect of Parameters for ROS Motion Planer and Navigation System for Indoor Robot

In the past decades, the service robot industry had risen rapidly. The office assistant robot is one type of service robot used to assist officers in an office environment. For the robot to navigate autonomously in the office, navigation algorithms and motion planners were implemented on these robots. Robot Operating System (ROS) is one of the common platforms to develop these robots. The parameters applied to the motion planners will affect the performance of the Robot. In this study, the global planners, A* and Dijkstra algorithm and local planners, Dynamic Window Approach (DWA) and Time Elastic Band (TEB) algorithms were implemented and tested on a robot in simulation and a real environment. Results from the experiments were used to evaluate and compare the performance of the robot with different planners and parameters. Based on the results obtained, the global planners, A* and Dijkstra algorithm both can achieve the required performance for this application whereas TEB outperforms DWA as the local planner due to its feasibility in avoiding dynamic obstacles in the experiments conducted.

Role of Machine Learning in WSN and VANETs

Dynamic nature of Vehicular Ad-hoc Networks (VANETs) and Wireless Sensor Networks (WSN) makes them hard to deal accordingly. For such dynamicity, Machine learning (ML) approaches are considered favourable. ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network. In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs. Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed. We have listed some of the ML techniques to take additional consideration of this emergent field. A summary is given for ML techniques application with their complexities to cover on open issues to kick start further research investigation. This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.

“MumCare”: An Artificial Intelligence Based Assistant

Bringing a new life to the world is a wonder to every mother. Experience faced by pregnant mothers vary from one pregnancy to another pregnancy. There is a vast amount of information available on the Internet and printed materials. Yet, this knowledge is too complex or lengthy and very few applications provide customized information to pregnant mothers. In a time where smart phones have become a necessity in our life, a mobile app is one of the easiest ways to obtain prenatal information. Hence, we have developed a mobile application to help pregnant mothers. This application includes an artificial intelligence (AI) based chatbot. AI chat bot communicates and guides the mother in a way that creates the illusion as if they are talking to their unborn child. The Spiral Model was used as the development methodology and the application was developed in an environment of continuous integration and deployment with GIT personal repository. This application was implemented using React Native and the Node.js. Chat bot was created with Dialogflow agent and integrated with the firebase through the Google cloud functions. Some existing applications were studied to identify the features and limitations of current pregnancy care mobile applications. This solution is realistic and successful and it has an upgradable model of growth. The rapid development of the Internet and mobile devices in the world has changed people's lifestyles. This mobile app will be helpful for pregnant mothers living in rural as well as in metropolitan areas alike and can enhance education and health.

Dynamic Modeling and Parametric Analysis of the Magnetic Stiffness on a Radial Heteropolar Rotor Magnetic Bearing (RMB)

This paper presents both the dynamic modelling and the study of the variation of certain physical parameters (pole area, gap and base current) that change the magnetic stiffness of a radial heteropolar Rotor Magnetic Bearing (RMB) aiming at the analysis of the magnitude of its control current for three different equilibrium points. The RMB is modelled as a fully uncoupled symmetric active 8-pole magnetic bearing. The analysis is performed using spectral cubes for a better visualisation of the posed problem. The saturation analysis of the RMB is also performed. At last, it is shown that a control current with the same magnitude as the base current is reached with minimum pole area and air gap s0 = 528 um.

Applying Multidimensional Geometry to Basic Data Centre Designs

Fog computing deployments are catching up by the day due to their advantages on latency and bandwidth compared to cloud implementations. Furthermore, the number of required hosts is usually far smaller, and so are the amount of switches needed to make the interconnections among them. In this paper, an approach based on multidimensional geometry is proposed for building up basic switching architectures for Data Centres, in a way that the most common convex regular N-polytopes are first introduced, where N is treated in an incremental manner in order to reach a generic high-dimensional N, and in turn, those resulting shapes are associated with their corresponding switching topologies. This way, N-simplex is related to a full mesh pattern, N-orthoplex is linked to a quasi full mesh structure and N-hypercube is referred to as a certain type of partial mesh layout. In each of those three contexts, a model is to be built up, where switches are first identified, afterwards, their downlink ports leading to the end hosts are exposed, along with those host identifiers, as well as their uplink ports leading to their neighboring switches, and eventually, a pseudocode algorithm is designed, exposing how a packet coming in from any given port of a switch is to be forwarded through the proper outgoing port on its way to the destination host by using the appropriate arithmetic expressions in each particular case. Therefore, all those algorithmic models represent how their corresponding switches may work when dealing with user data traffic within a Data Centre, guiding it towards its destination.