Modular multilevel converters (MMCs) are an emerging and promising option for medium voltage direct current (MVDC) of all- electric ships. In order to improve the stability of the MVDC transmission system for ships, this paper presents a new control inputs-based Lyapunov strategy based on feedback linearization. Firstly, a set of dynamics equations is proposed based on separating the dynamics of AC-part currents and MMCs circulating currents. The new control inputs can be obtained by the use of feedback linearization theory applied to the dynamic equations. To complete the dynamic parts of the new control inputs from the viewpoint of MVDC system stability, the Lyapunov theory is designed some compensators to demonstrate the effects of the new control inputs on the MMCs state variable errors and its dynamic. In addition, the carrier phase shifted modulation strategy is used because of applying the few number of converter modules to the MVDC system for ships. Moreover, relying on the proposed control strategy, a simulation model is built in MATLAB/SIMULINK software, where simulation results are utilized to verify the validity of proposed control strategy in the MMC-based MVDC system for ships.

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

Single image dehazing algorithm based on Dark Channel Prior (DCP) is widely known. More and more image dehazing algorithms based on DCP have been proposed. However, we found that it is more effective to use DCP in the RAW images before the ISP pipeline. In addition, for the problem of DCP failure in the sky area, we propose an algorithm to segment the sky region and compensate the transmission. Extensive experimental results on both subjective and objective evaluation demonstrate that the performance of the modified DCP (MDCP) has been greatly improved, and it is competitive with the state-of-the-art methods.

In this paper we establish a secure communication model where eavesdropper and intended receiver have multiple antennas. We use cooperation and jamming to achieve physical layer security. First, we study how to allocate power between the information bearing signal and the jamming signal. Second, based on this model, we also jointly optimize both the information bearing signal weights and the jamming signal weights to improve physical layer security. The optimal power allocation and the weights are obtained via an iteration algorithm to maximize the secrecy rate. Comparing with equal power allocation and some other different methods, it shows that using cooperative relaying and jamming can significantly improve the physical layer security from the simulation results.

Federated learning is a promising strategy for indoor localization that can reduce the labor cost of constructing a fingerprint dataset in a distributed training manner without privacy disclosure. However, the traffic generated during the whole training process of federated learning is a burden on the up-and-down link, which leads to huge energy consumption for mobile devices. Moreover, the non-independent and identically distributed (Non-IID) problem impairs the global localization performance during the federated learning. This paper proposes a communication-efficient FedAvg method for federated indoor localization which is improved by the layerwise asynchronous aggregation strategy and layerwise swapping training strategy. Energy efficiency can be improved by performing asynchronous aggregation between the model layers to reduce the traffic cost in the training process. Moreover, the impact of the Non-IID problem on the localization performance can be mitigated by performing swapping training on the deep layers. Extensive experimental results show that the proposed methods reduce communication traffic and improve energy efficiency significantly while mitigating the impact of the Non-IID problem on the precision of localization.