Based on the development of hybrid AC/DC microgrid, considering the objectives of microgrid life-cycle cost, self-balancing rate and converter losses, an optimisation configuration model of a hybrid AC/DC microgrid is established here. Based on the different model of EVs to connect with grid, the matching control strategies for the model are proposed. The elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimisation configuration model. With the example system simulation, the demand response function of EVs is analysed, and the impacts of different EVs charging modes on optimisation configuration results of the hybrid AC/DC microgrid are studied and discussed. The rationality and effectiveness of proposed models and strategies are verified.

As software system becomes complex and large, some phenomena, performance degradation, error service, and even the abrupt system outage, have been found in the operating system, web server, and even in the security system. These phenomena can be called software ageing problems. In order to make software re-enter into a robust state, some prediction methods need be used to find an optimal time to do rejuvenation. Since the prediction method used now is linear or non-linear, a hybrid method is proposed in this work to improve the prediction accuracy. The proposed method contains three phases. Firstly, a linear method is applied to capture the linear characteristics of resource consumption series. Secondly, a non-linear method is used to fit non-linear features in the original data and residual data. Finally, the characteristic of the linear and non-linear part will be fitted by a non-linear model. In the experiments, the results show that the proposed method outperforms the other two single models.

Performances of the regularised zero forcing (RZF) precoder, rapid numerical algorithm (RNA) precoder and the truncated polynomial expansion (TPE) Precoder are compared and analysed for massive multiple-input multiple-output (MIMO) wireless system in a downlink scenario. The achievable data rates, bit error rates and signal-to-interference-to-noise ratio were investigated. The simulated results agree with the theory of massive MIMO system. Also, the RZF precoder and the RNA precoder perform better in comparison to TPE precoder.

How to effectively detect abnormal electricity consumption behaviour from a large-scale electrical load data is very important to smart grid. An abnormal electricity consumption analysis method based on density clustering is proposed. First, the similar users located in fix area are clustered in accordance with the electricity consumption characteristics. Then, on the basis of electricity consumption data sequence, the outlier electricity consumption for the similar users are obtained with density clustering. The matching degrees of these outliers can be calculated based on the similar user electricity consumption model and historical electricity consumption model. Finally, according to the threshold value, the abnormal electricity consumption can be discriminated with the comprehensive support degree. The simulation results show that this method can effectively identify the abnormal electricity consumption behaviour.

Distributed generation (DG) has seen tremendous growth to meet the needs of ever-increasing energy demand. Most of these distributed sources are renewable in nature and connected at the consumer end. The increasing penetration of DG sources has made their control and operation complex. One of the issues that are responsible for this increased complexity is islanding. This study presents a new islanding detection method (IDM) that is based on deep learning approach, using a convolution neural network (CNN). The proposed method first converts time-series data to images and then uses them to train and test the designed CNN. A CNN is specifically designed to perform islanding detection. The results using the designed CNN are compared with IDMs based on artificial NN and support vector machine. These comparisons show that islanding detection performed using deep learning technique has better detection accuracy. Also, the proposed method performs well even for noisy data.

There have been growing needs for text processing, such as classifying, retrieving and clustering. The foundation of such a process is to extract features, which can best describe the text. Great progress has been made in text modelling. However, most of the text modelling methods are based only on the content, nor only on the attributes. Although there have been some combined models proposed in recent years, the lack of universality limits such models. In this study, the authors propose a uniform attribute-content model, which uses the attributes to influence the content feature extraction process. They design the attributes as a special filter to each feature extracted from the content. Thus the mixed features contain both content information and attribute information, which can describe the text more precise. They also propose a Monte Carlo method to solve this model. Experimental results on the Enron email dataset demonstrate the effectiveness of the authors’ proposed models.

In the letter, a wireless energy harvesting (WEH) multicast network is proposed. Two source nodes communicate with their respective destination nodes via a WEH relay node. Adopting the power splitting relaying (PSR) protocol and analogue network coding (ANC), the relay node simultaneously harvests the energy and receives the information from the signals transmitted by the two source nodes in the first time slot , and retransmits the amplified signal to the destination nodes in the second time slot using all harvested energy. The analytical expressions of the ergodic sum-rate of the proposed network are derived. Numerical results corroborate the derived expressions and show that the proposed scheme nearly doubles the maximal ergodic sum-rate in comparison with the conventional unicast four time slots WEH scheme.

Reconfigurable resource utilisation is a key factor that impacts the performance of reconfigurable instruction set processors (RISPs), which can customise their instruction set for special applications. In this study, the authors propose a novel method to improve reconfigurable resource utilisation of RISPs based on field programmable gate array (FPGA) chips. The method provides a graph multi-colouring allocating algorithm based on a simplified reconfigurable resource model. In the model, they construct three kinds of FPGA-based coarse-grained reconfigurable resources and abstract their attributes mainly through their location and area. The authors’ algorithm employs a graph multi-colouring method to allocate the coarse-grained reconfigurable resources to custom instructions for RISPs. Experimental results demonstrate that their model and algorithm reinforce a state-of-the-art resource utilisation method for RISPs, and reveals some interesting regularities which provide creditable new insights of reconfigurable resource utilisation.

In this work, the authors report positive results regarding their measurements of anomalous thrust forces in a resonant microwave cavity without dielectrics associated with a magnetron. They measure such forces and successfully compared these values with their corresponding theoretical values, based on the hypothesis of preexisting state of generalised quantum entanglement in which the momentum conservation principle is satisfied by the non-local interaction between the internal charge carriers of the system and the environment. It is explained why the system can provide us with a novel technology using thrust without ejecting any kind of propellant. It is also reported a novel property related to the external force induction projected from the system. They show that their theoretical calculations also present a good agreement with the experimental measurements of other authors working with such types of EM-Drives. The control and enhancement of the effect can allow the viability of a new technology of spatial transportation and the development of devices for force induction at a distance.

A novel neural network algorithm for indirect measurement of the polished rod load of the beam-pumping unit is proposed. The dynamometer card is a two-dimension graph of polished rod load versus horse head position, and it is widely used for working state monitoring of pumping unit. The drift problem of load sensor makes the direct measurement of polished rod load not economical and practical, so indirect measurement of polished rod load is crucial. Through the analysis of the mechanical characteristics of the pumping unit, a neural network with physical meaning (physical network) is designed. The experiment shows that the physical network can simulate the energy transfer process properly with only 18 training data items, which is about 1% of other data-driven models, and maintains good performance under different down-hole conditions.

In view of the need of near-space vehicle energy management system to adapt to wide input and wide output voltage range, this study presents a high-efficiency buck–boost converter and its control strategy suitable for wide voltage range. In this study, a three-mode control strategy of buck–boost circuit based on analogy circuit is proposed, which realises the smooth switching between buck–boost converter in boost, buck, and through mode. On this basis, a buck–boost power regulation unit adopting maximum power tracking algorithm control is developed. Based on the realisation of standardisation of buck–boost power conditioning unit, a power regulator is constructed by connecting two standardised units in series to achieve efficient output of power regulator in wide voltage range. In this study, the startup strategy, normal working control strategy, over-voltage and over-current protection strategy of series converter are put forward, so as to ensure the normal operation of series converter. The power regulator developed in this study is based on buck–boost topology, its output voltage is 150–300 V, and its power density reaches 2000 W/kg. The power regulator with small size, high power, high voltage, low component stress meets the need for high-power density and wide voltage range energy managers of near-spacecraft energy systems.