A task selection method for multi-faced static phased array radar resource management in dynamically changing environments using recomposable restricted finite state machines is presented. Restricted finite state machines allow the design of a composed finite state machine with resource limitations by restricting some of the inputs. Recomposable restricted finite state machines allow the state space of a finite state machine to change dynamically, which allows the modelling of a dynamically changing environment. Applying dynamic programming to restricted finite state machines yields optimal policies for a given cost function and applying breadth-first search or limited breadth-first search with fixed depth yields suboptimal solutions for the current state. The authors model a task selector for the radar in an overloaded battlefield situation using recomposable restricted finite state machines and obtain a radar resource allocation policy using dynamic programming when the environment changes dynamically and the resources are limited. The suboptimal solution for the current state is obtained using heuristic methods: breadth-first search, or limited breadth-first search in the task selector for large-scale problems. Furthermore, the authors consider distributed architectures for multi-radar systems with communication channels. The results show that their approach performs well from the standpoints of both computational time and performance.

Here, a new topology for direct drive wind turbines (DDWTs) with a new power electronics interface and a low-voltage generator is presented. In the presented power electronics interface, the grid-side converter is replaced by a boost current source inverter (CSI) which eliminates the required dc-bus capacitors, resulting in an increase in the system lifetime. The inherently required dc-link inductor for this topology is eliminated by utilising the synchronous inductance of the permanent magnet synchronous generator. The control technique used for the developed DDWT is described here. The boost CSI enables conversion of a low dc voltage to a higher line-to-line voltage creating an indirect boost matrix converter, and thus, enabling the implementation of a low-voltage generator. This further enables a reduction in the number poles required in direct drive wind generators. The feasibility of the proposed generator-converter topology for DDWTs and its controllers are verified through simulations and experimental results on a laboratory scale 1.5 kW, 240 V set-up.

This study deals with the rigorous evaluation of computer numeric control (CNC) machining centre for the torque computation. A novel model has been proposed for the CNC feed drive system investigations. Initially, mathematical equations have been derived for the CNC feed drive system explorations. Next, the torque has been calculated over three factors namely: lead, feed force, and accelerating speed. Further, these proposed equations have been implemented over the deployed model. Finally, comprehensive analytical investigations have been made to highlight the relation among static toque with feed force and rated torque with lead values.

As in traditional computers, future bio-computers might feature the analogous mechanism to carry out a set of arithmetic or logical operations sequentially. In digital computers, memory is essential for which digitised information is encoded, stored, and retrieved. A stored memory unit in the computer is almost constructed by metal–oxide–semiconductor field-effect transistors. In the future, realising the specific data stored memory unit in the biocomputer is essential. However, it is not easy to realise that mechanism for data memorisation in biological systems. The authors propose a structure of a fundamental memory unit necessary with input/output configuration for data read and write for the biocomputer.

This research paper presents an advanced approach to enhance the short-term wind power prediction based on artificial intelligence techniques. A high-quality wind power prediction is essential for power system planning, operation, and control. Thus, a new novel approach has been developed to improve the quality and reliability of the calculated results by integrating advanced time series processing method and the extreme learning machine technique. Moreover, historical records are utilised from numerical weather information and multiple observations points close to real wind farm sites within Australia regions. The wind speed is assessed by using the developed model in the first stage, and then the wind power and capacity factor is calculated using wind power–speed curve for each observation site. Artificial neural network, fuzzy logic (adaptive neuro-fuzzy inference system), and support vector machine models are used for model verifications, validations, and practical applications. The developed model is tested using real wind measurements by Bureau of Meteorology, 15 selected weather stations corresponded to the locations of nearby real wind farm sites in Australia. The demonstrated results and performance indicators, e.g. root mean square error and mean absolute error are compared with Khalid, persistence, and Grey predictor models for validations and verifications reasons. As the potential gains over other techniques, the proposed model has found more efficient and superior for wind power estimation and prediction than other developed conventional methods and models, which in turn improves the power system performance, and reduces the economic impacts.

Time synchronisation is one of the key issues to realise remote detection for multi-static radar. With the condition that the receivers are commonly located at different distances from the transmitter, a high-precision time synchronisation scheme for multi-static radar with mixed microwave and troposcatter channels is proposed. In order to prove the feasibility of transmitting pulse signals in microwave channel, the jitter of the signals has been tested, and the transmission loss of the troposcatter channel has been calculated. Finally, the accuracy of the proposed model is analysed, which shows that the proposed scheme is feasible and can be a backup for time synchronisation for multi-static radar.

This study reports a simple approach to emerging multi-double-scroll attractor from a variable-boostable chaotic system via multi-level pulse excitation, which is achieved by replacing a constant controller with a multi-level pulse function. To intuitively show the simple and feasible approach, a four-double-scroll chaotic attractor coined in such a system with four-level pulse excitation is taken as a paradigm.

The gradient is an important property in an image. According to the characteristics of image gradient histogram (image gradient magnitude distribution), Gamma distribution model is near to the actual distribution, so Gamma mixture model is used to fit natural image gradient distribution. First, an image can be divided into edge region and non-edge region on the aspect of the gradient; the authors assume that each region obeys sub-Gamma distribution with different parameters. Then, expectation maximisation (EM) algorithm is used to estimate the parameters of each part. Finally, the accuracy of the fitting result of the entire gradient distribution is verified by the correlation coefficient and the validity of the estimated gradient magnitude distribution of non-edge region and edge region is verified by the edge-detection experiment with different threshold. This work can select Canny edge-detector high threshold adaptively, which can improve algorithm automatic level.