Key technologies of active power filter for aircraft: a review

Active Power Filter (APF) is not only an advanced technology to improve power quality and purify power system pollution but also a good approach to solve electrical problems of an advanced aircraft such as harmonic, reactive power and unbalanced load. However, there are still some specific problems for the application of aeronautic APF in practice. Based on current research on aeronautic APF, this paper reviews three key technologies where APF can be used in aircraft AC power supply system, including the acquisition method of reference current, the strategy of APF current control and the main circuit topology.  Consecutively, the features of current aeronautic APF research are summarized, and the future research directions are also suggested

A Study on Automatic Control Principle Courseware Based on MATLAB

The course of automatic control principle needs to draw a lot of curves, but actually it is difficult to achieve. In order to solve the problems that the diagrams of automatic control principle is difficult to draw and the knowledge is difficult to understand, this paper presents a kind of automatic control principle courseware based on VISUAL C++ and MATLAB hybrid programming. The hybrid programming methods of VISUAL C++ and MATLAB are discussed in this paper, then analyzes the concrete realization method of VISUAL C++ calling MATLAB engine. In order to get a friendly user interface, the courseware using VISUAL C++ to write GUI(Graphical User Interface) and related data processing utilizing the MATLAB control system toolbox. Through MATLAB engine, this courseware can easy to draw the Bode diagram, Nyquist curve, Root Locus diagram and so on. The courseware make full use of the advantages of VISUAL C++ and MATLAB, has a friendly GUI and basically achieved all functions of MATLAB, which are convenient for teaching. The courseware designed in this paper can help the student to study the principle of automatic control and can improve the study effect to a certain extent. Keywords: VISUAL C++, MATLAB engine, Hybrid programming, Coursewar

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN

Dynamic routing in software-defined networking (SDN) can be viewed as a centralized decision-making problem. Most of the existing deep reinforcement learning (DRL) agents can address it, thanks to the deep neural network (DNN)incorporated. However, fully-connected feed-forward neural network (FFNN) is usually adopted, where spatial correlation and temporal variation of traffic flows are ignored. This drawback usually leads to significantly high computational complexity due to large number of training parameters. To overcome this problem, we propose a novel model-free framework for dynamic routing in SDN, which is referred to as spatio-temporal deterministic policy gradient (STDPG) agent. Both the actor and critic networks are based on identical DNN structure, where a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) with temporal attention mechanism, CNN-LSTM-TAM, is devised. By efficiently exploiting spatial and temporal features, CNNLSTM-TAM helps the STDPG agent learn better from the experience transitions. Furthermore, we employ the prioritized experience replay (PER) method to accelerate the convergence of model training. The experimental results show that STDPG can automatically adapt for current network environment and achieve robust convergence. Compared with a number state-ofthe-art DRL agents, STDPG achieves better routing solutions in terms of the average end-to-end delay.Comment: 6 pages,5 figures,accepted by IEEE ICC 202

A Study on Comprehensive Experiment of Flight Technology Based on OBE

The research is financed by the ministry of education special task of humanities and social sciences research (Grant Nos. 17GDJC017) and teaching reform project of Binzhou University (Grant Nos. BYJYYB201618, BYJY2D201802). Abstract tFrom the perspective of practical ability of students' actual flight training and flight work, this paper studies on the comprehensive experimental system of flight technology. Then analyzes the problems and insufficiency existing in the original experiment construction, reforms the traditional experimental construction mode. Based on OBE, through build the relation matrix between knowledge, practice ability, experimental project and course, completed a strong comprehensive and perfect concrete design of comprehensive flight technology experiment. At last, problems in concrete implementation are discussed, and the advantages to promote students’ ability of practice and the ability of using knowledge comprehensively have been proved by practice. Keywords: OBE, Comprehensive experiment, Flight technology, Relation matrix DOI: 10.7176/JEP/10-36-02 Publication date: December 31st 201

Densely Knowledge-Aware Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of data while Trans has three transformer blocks for extracting the global patterns of data. Besides, to enable dense mutual supervision between lower- and higher-level semantic information, this article adapts densely dual self-distillation (DDSD) for mining rich regularizations and relationships hidden in the data. Experimental results show that compared with 5 state-of-the-art self-distillation variants, the proposed DDSD obtains 13/4/13 in terms of “win”/“tie”/“lose” and gains the lowest-AVG_rank score. In particular, compared with pure ResMulti-Trans, DKN results in 20/1/9 regarding win/tie/lose. Last but not least, DKN overweighs 18 existing MTSC algorithms on 10 UEA2018 datasets and achieves the lowest-AVG_rank score

CapMatch: Semi-Supervised Contrastive Transformer Capsule With Feature-Based Knowledge Distillation for Human Activity Recognition

This article proposes a semi-supervised contrastive capsule transformer method with feature-based knowledge distillation (KD) that simplifies the existing semisupervised learning (SSL) techniques for wearable human activity recognition (HAR), called CapMatch. CapMatch gracefully hybridizes supervised learning and unsupervised learning to extract rich representations from input data. In unsupervised learning, CapMatch leverages the pseudolabeling, contrastive learning (CL), and feature-based KD techniques to construct similarity learning on lower and higher level semantic information extracted from two augmentation versions of the data“, weak” and “timecut”, to recognize the relationships among the obtained features of classes in the unlabeled data. CapMatch combines the outputs of the weak-and timecut-augmented models to form pseudolabeling and thus CL. Meanwhile, CapMatch uses the feature-based KD to transfer knowledge from the intermediate layers of the weak-augmented model to those of the timecut-augmented model. To effectively capture both local and global patterns of HAR data, we design a capsule transformer network consisting of four capsule-based transformer blocks and one routing layer. Experimental results show that compared with a number of state-of-the-art semi-supervised and supervised algorithms, the proposed CapMatch achieves decent performance on three commonly used HAR datasets, namely, HAPT, WISDM, and UCI_HAR. With only 10% of data labeled, CapMatch achieves F1 values of higher than 85.00% on these datasets, outperforming 14 semi-supervised algorithms. When the proportion of labeled data reaches 30%, CapMatch obtains F1 values of no lower than 88.00% on the datasets above, which is better than several classical supervised algorithms, e.g., decision tree and k -nearest neighbor (KNN)