This study presents a novel adaptive management strategy for power flow in standalone hybrid power systems. The method introduces an on-line energy management by using a hierarchical controller between three energy sources: photovoltaic (PV) panels, battery storage and proton exchange membrane fuel cell. The proposed method includes a feed-forward, back-propagation neural network controller in the first layer, which is added in order to achieve the maximum power point for the different types of PV panels. In the second layer, a fuzzy logic controller has been developed to optimise performance by distributing the power inside the hybrid system and by managing the charge and discharge of the current flow. Finally, and in the third layer, local controllers are presented to regulate the fuel cell/battery set points in order to reach to best performance. Moreover, perturb and observe algorithm with two different controller techniques – the linear proportional-integral (PI) and the non-linear passivity-based controller – are provided for a comparison with the proposed maximum power point tracking controller system. The comparison revealed the robustness of the proposed PV control system for solar irradiance and load resistance changes. Real-time measured parameters and practical load profiles are used as inputs for the developed management system. The proposed model and its control strategy offer a proper tool for optimising the hybrid power system performance, such as the one used in smart-house applications.

Recent mobile handhelds, for example, smart-phone, can be charged wirelessly using the traditional wireless communication technologies as well as a new energy-harvesting circuit. In particular, Bluetooth with a charging circuit enables a short-range wireless charging of a mobile handheld. This study presents an overview of power-charging structures and proposes a new charging and harvesting circuit based on schottky diode with a real commercial film-type antenna on mobile terminal. The authors’ new circuit improves the charging efficiency approximately by 160%, that is, 0.45 mA rather than 5 mA for a current Smartphone in the consumer market.

The precise measurement and analysis of human movements is an essential step in biomechanical research used in sports or medicine. Measurement systems used for motion tracking should be non-invasive, safe to use, widely customisable and cost-efficient. In this study, complete design, development and evaluation of a high-speed optical motion tracking and analysis system is described. The system aims to analyse movements for sports and medical applications. The novelty of the proposed system is its design, which is based on visible light light-emitting diode (LED) markers, rather than infrared markers that are commonly used, and a pair of high-speed digital cameras. Calibration procedures and a super-resolution marker model are introduced, ensuring sub-pixel marker centre detection which results in higher three-dimensional reconstruction accuracy. Evaluation of the system included an accuracy test of the proposed system on static and moving objects with known dimensions, followed by analysis of kinematic data obtained in dynamic conditions while measuring human gait. The evaluation results are presented, and conclusions about system performance with possible improvements are discussed.

This study presents a novel technique for the measurement of insertion depth of an epidural Tuohy needle in real-time. A wireless camera is used which transmits video images during insertion to a host computer. The computer contains the image processing algorithm to detect the visible needle in the image and measures the length. The measurement is done by HSV background removal, colour comparison and using RGB intensity profile to locate 10 mm markings on the Tuohy needle shaft. The visible length is then subtracted from the known length of the needle to calculate the depth of the needle tip. The camera can be placed in the operating theatre between 50 and 100 cm away from the needle insertion site. Wireless camera is beneficial since it minimises disturbance to the anaesthetist and the patient and avoids ethical and sterility concerns which would be caused by bringing additional equipment into the hospital room. The speed of the image processing technique is 10 frames per second with a maximum error of ±3 mm. The image processing failure rate was 3 frames out of 150 which gave an overall reliability of 97.8% during insertion. Also this enables continuous needle depth monitoring during insertion by storing measurements into a data file. The purpose of measuring needle depth in real time is to precisely place the needle in the epidural space.

Increased moisture content within an oil-paper insulation system can significantly reduce its life expectancy, and for oil impregnated paper condenser type bushings, it is the most common cause of failure. Distinction between moisture contents using traditional power frequency tests of dissipation factor and capacitance can be difficult, particularly at ambient temperatures. Dielectric frequency response is becoming an established technique to measure the dissipation factor and capacitance of a bushing, but to date an accurate model to evaluate the condenser moisture content has not been presented using this technique. In this study, a dielectric frequency response bushing model is proposed and finite element method software is used to simulate the variation in dissipation factor and capacitance of a bushing with varying moisture content, as a function of frequency and temperature. The modelled results are compared with measurements reported in the literature and from the authors’ own field measurements, where a good agreement is demonstrated. It is shown that the distinction in dissipation factor between moisture contents at frequencies <0.1 Hz is in excess of ten times greater than at 50 Hz. The proposed model can be used by practitioners to evaluate moisture content within the condenser insulation and distinguish between moisture contents ranging from 0.2 to 4.0%.

In this study, one acquisition system of one channel is introduced to obtain the biological brain signals. Such system consists of a low-pass filter, an amplifier, a high-pass filter and a data acquisition board. A novel slopes algorithm with guaranteed bounded output is proposed for the approximation of the brain signals. The proposed method is compared with both the least-squares and nearest-neighbour algorithms.

This study presents a novel sensing methodology with two optimal condition-based fusion algorithms for attitude estimation, using low-cost micro-machined gyroscopes, accelerometers and magnetometers. The proposed methodology named two-step optimal filter is composed of an optimal filter and fast determination algorithm. The filter is designed as sensor-based Kalman filter, which is augmented by a fuzzy rule to adjust the parameters on line to yield optimal measurements of accelerometers and magnetometers. Then, the fast second estimator of the optimal quaternion algorithm is described to determine the orientations. Meanwhile, adaptation architecture is implemented to yield robust performance, even when the vehicle is subject to strong accelerations or ferromagnetic disturbed. The new construction of attitude estimation algorithm is easy to be implemented, the precise, robustness and efficient are compared with the common methodology. Experimental results are provided for a remotely operational vehicle test and the performance of the proposed filter is evaluated against the output from a conventional filter.